diff --git a/ACTION_REPAIR_20260918.md b/ACTION_REPAIR_20260918.md new file mode 100644 index 0000000000000000000000000000000000000000..3abc273f9dd1bf2898d78af781ccba1228818a5a --- /dev/null +++ b/ACTION_REPAIR_20260918.md @@ -0,0 +1,77 @@ +# Mocap action repair and review — 2026-09-18 + +Scope: all 218 published parquet segments/episodes: main motherboard25, +pushT68, rope70, toy10; old motherboard32 and pushT4; validation9. +Raw sensor poses, timestamps, source frame IDs and videos are preserved. + +## Review first + +Download `action_sample25.html` and open it in a browser. It contains25 +stratified examples, original camera videos and raw/repaired pose traces. +Video playback requires an internet connection. `action_sample25.json` +records the selected examples and their evidence. `action_review.html` +covers every unresolved event occurrence in published segments. + +**A tracking jump and genuine rapid out-and-back motion can have identical +pose sequences. Smooth reconstruction does not prove tracking loss.** +Synthetic reconstruction benchmarks measure interpolation accuracy conditional +on an error being present; they do not establish real-world detection precision. +The negative-control report documents this limitation. + +The strict review policy admits automatic HIGH repairs only where replaced +observations have independent loss evidence: declared tracking gaps or invalid +raw poses. Other proposed repairs remain MEDIUM pending human review; LOW +events are not reconstructed. Persistent branch proposals invalidate the whole +identified interval, including its smooth interior, not just boundary jumps. +`action_lost_track_*` means **suspected/review-required tracking corruption**, +not a confirmed ground-truth diagnosis. Genuine fast movement may remain +unchanged but masked under the existing physical-action limits. + +## Columns and action sidecars + +- `sensor_{left,right}_pose`: unchanged original pose. +- `sensor_{left,right}_pose_repaired`: proposed reconstructed pose; consult masks. +- `pose_{left,right}_valid`, `_repaired`, `_repair_confidence`, + `_repair_event_id`, `_repair_method`: pose provenance. +- `action`:14 floats, next-frame absolute left XYZ+quaternion(xyzw), then + right XYZ+quaternion(xyzw), in the same world coordinates as the parquet. + The final row repeats the final pose and is invalid, with no next transition. +- `action_valid_{left,right}`: the authoritative per-hand training mask. +- `action_repaired_*`, `action_repair_confidence_*`, `action_lost_track_*`, + `action_long_lost_track_*`: transition provenance. Long means over60 native + frames, retaining the original event duration across segment boundaries. + +Confidence codes:0 unchanged/unclassified,1 LOW,2 MEDIUM,3 HIGH. +Confidence alone is not a validity mask. + +Each parquet has left/right `actions_native/...npz` and +`actions_fps15/...npz`. Each sidecar stores9D world-frame translation delta +plus relative-rotation6D (first two matrix columns, column by column). +Native actions connect k→k+1.15fps actions connect k→k+2 at local rows0,2,4…; +their validity is the AND of both native transitions. No action crosses a +published segment boundary. + +Training consumers must enforce both hand masks: `action_full` rejects any +window containing an invalid transition; `action_causal` excludes invalid +transitions from its action loss. Parquet masks do not enforce this by themselves. +These added columns are not a conversion of the entire repository to a new +LeRobot storage version. + +## Force and reproducibility + +The old-data force update is separate:36 episodes rebuilt using V8 from +released lossy tactile MP4 because the raw May H5 files are gone. See the force +recovery documentation and each `.force.json` sidecar. MP4 recovery is not +bit-equivalent to raw-H5 processing, and absolute force accuracy on React is +not established. Action publication retains those newly rebuilt force fields. + +`action_publication_manifest.json` records the frozen source mapping, source +hashes, output hashes, coordinate conversions and preceding force revision. +`repair_audit/` contains reconstruction benchmarks, negative controls, +build configurations and event counts. Counts of source events, published +event occurrences and action transitions are different units. + +The source implementation is included under `action_repair_tools/`. It uses +NumPy, SciPy, PyArrow and huggingface_hub. Candidate builds remain separate +from raw release inputs. Review decisions must identify their source episode +as well as the event; same local frame IDs can occur in different episodes. diff --git a/action_publication_manifest.json b/action_publication_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6b53b8ca0ea3e624145eaff75995ad71fe29985a --- /dev/null +++ b/action_publication_manifest.json @@ -0,0 +1,3075 @@ +{ + "schema_version": 1, + "revision": "396b72f02535b653bb07fac4cd1383c46872b5b3", + "base_root": "/media/yxma/Disk1/twm/review/mocap_repair_stage2_2026-09-18/publication_input", + "counts": { + "data/motherboard": 25, + "data/pushT": 68, + "data/rope": 70, + "data/toy": 10, + "data/validation": 9, + "old_data/motherboard": 32, + "old_data/pushT": 4 + }, + "episodes": [ + { + "remote_path": "data/motherboard/meta/2026-09-11/episode_000.parquet", + "base_sha256": "c65cf49f187832c6d9d70371a13a764a47829e004afc3127a69e9a3e6aa729ff", + "source_path": 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a/action_repair_tools/README.md b/action_repair_tools/README.md new file mode 100644 index 0000000000000000000000000000000000000000..68523180940d647ab1be9ab8718d2b49fc4fa59d --- /dev/null +++ b/action_repair_tools/README.md @@ -0,0 +1,39 @@ +# Action repair tools + +Run from this directory after `python -m pip install -r requirements.txt`. +The package uses minimal namespace initializers to avoid pulling in unrelated +recording/camera dependencies. It contains no model weights or credentials. + +The September18 build used the frozen **uncut source parquet tree**, its +`pose_gaps.json` files and the input manifests in `../repair_audit/`. +The public segmented parquets alone do not reproduce context that was cut +away; do not treat a segment boundary as a new tracker dropout. + +Example with your original source tree and a NEW candidate output directory: + +```bash +python -m twm.scripts.build_mocap_repair_candidates --output /path/to/candidates audit --source /path/to/source +python -m twm.scripts.build_mocap_repair_candidates --output /path/to/candidates benchmark --max-intervals 700 --seed 17 +python -m twm.scripts.build_mocap_repair_candidates --output /path/to/candidates build --branch-review --require-loss-evidence +python -m twm.scripts.build_mocap_repair_candidates --output /path/to/candidates verify +python -m twm.scripts.propagate_mocap_repairs --help +``` + +For the exact September18 benchmark length set `(1,2,3,4,5,20,60)`, call +`run_benchmarks(candidate_root, anomaly_lengths=(1,2,3,4,5,20,60), max_intervals=700, seed=17)`. +The CLI default length set is smaller. Build the separate validation source +with `--task validation` during audit. Use the propagation inventory to map +every published segment by source frame ID, not inferred filename offsets. + +`propagate_mocap_repairs export` regenerates native/15fps sidecars and the +lightweight HTML review, including25 examples. Pass a pinned video-file +inventory to `--remote-videos`. The older full camera-rendering `review` +subcommand requires the full MultimodalData repository and is not bundled. + +Review decisions use `task/date/source_episode::local_event_id`, mapped from +the published segment through the inventory. `keep_raw`, `accept_repair`, +`invalidate`, and `unsure` are supported; accepting one event never implicitly +approves another event with the same frame indices in a different episode. +Re-export actions after applying decisions. The publisher requires a bound +force-commit receipt and an unchanged HF parent commit; it never uploads unless +explicitly invoked with `--publish`. diff --git a/action_repair_tools/requirements.txt b/action_repair_tools/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..38c5fe0369fc061f6f21ce45f0e772ff38b2c92e --- /dev/null +++ b/action_repair_tools/requirements.txt @@ -0,0 +1,4 @@ +numpy +scipy +pyarrow +huggingface_hub diff --git a/action_repair_tools/twm/__init__.py b/action_repair_tools/twm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9fe1e23652cfc8be95b6d07120bca45a7e3cff84 --- /dev/null +++ b/action_repair_tools/twm/__init__.py @@ -0,0 +1 @@ +"""Minimal standalone namespace for the published action-repair tools.""" diff --git a/action_repair_tools/twm/react_preprocess/__init__.py b/action_repair_tools/twm/react_preprocess/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..50890e7c4f10e5e7687b31cddd485738b881d9c2 --- /dev/null +++ b/action_repair_tools/twm/react_preprocess/__init__.py @@ -0,0 +1 @@ +"""Repair-only package; full recording/video preprocessing is not bundled.""" diff --git a/action_repair_tools/twm/react_preprocess/mocap_branch.py b/action_repair_tools/twm/react_preprocess/mocap_branch.py new file mode 100644 index 0000000000000000000000000000000000000000..5f69cba12964fed4fd1ffd7d2a4c27896c5c556b --- /dev/null +++ b/action_repair_tools/twm/react_preprocess/mocap_branch.py @@ -0,0 +1,181 @@ +"""Conservative body-frame branch proposals, separate from training admission. + +A pose is a body-to-world transform T. Marker symmetry changes it to T @ B, +so correction must be right multiplication by inverse(B), including the body +translation lever arm. Subtracting a world offset destroys genuine motion. + +Paired boundaries supply two independent estimates of B using only immediate +outer context. This never interpolates the interior of a persistent interval. +Even matching return pairs are hypotheses: real abrupt out-and-back motion can +be observationally indistinguishable. All results therefore remain MEDIUM; +method/task-specific validation and any human decision belong to the caller. +""" +from __future__ import annotations + +from dataclasses import dataclass, replace +from typing import Iterable + +import numpy as np +from scipy.spatial.transform import Rotation + + +@dataclass(frozen=True) +class BranchConfig: + rotation_seed_deg: float = 30.0 + coupled_rotation_deg: float = 12.0 + coupled_translation_mm: float = 20.0 + context_frames: int = 5 + max_interval_frames: int = 900 + transform_rotation_tolerance_deg: float = 3.0 + transform_translation_tolerance_mm: float = 5.0 + boundary_rotation_tolerance_deg: float = 3.0 + boundary_translation_tolerance_mm: float = 5.0 + max_step_rotation_deg: float = 30.0 + max_step_translation_mm: float = 50.0 + + +@dataclass(frozen=True) +class BranchCandidate: + start: int + end: int + pose: np.ndarray + branch_rotation_xyzw: np.ndarray + branch_translation_m: np.ndarray + evidence: dict[str, float | int | bool | str] + confidence: str = 'MEDIUM' + + +def _predict_next(context: np.ndarray) -> tuple[np.ndarray, Rotation]: + """One-step prediction; reversed context yields a backward prediction.""" + rotations = Rotation.from_quat(context[:, 3:]) + angular = np.median((rotations[1:]*rotations[:-1].inv()).as_rotvec(), axis=0) + velocity = np.median(np.diff(context[:, :3], axis=0), axis=0) + return context[-1, :3] + velocity, Rotation.from_rotvec(angular)*rotations[-1] + + +def _transform(expected: tuple[np.ndarray, Rotation], observed: np.ndarray): + xyz, rotation = expected + return rotation.inv().apply(observed[:3]-xyz), rotation.inv()*Rotation.from_quat(observed[3:]) + + +def find_branch_candidates( + pose: np.ndarray, + known_gaps: Iterable[tuple[int, int]] = (), + *, + trusted: np.ndarray | None = None, + config: BranchConfig | None = None, +) -> list[BranchCandidate]: + """Propose complete paired-return intervals without changing input poses. + + ``trusted`` optionally identifies independently trusted outer anchors. + Without it, anchors are only screened for finite, contiguous, quiet local + observations and evidence records that limitation. Declared gaps and any + nonfinite/zero-quaternion rows exclude both anchors and branch interiors. + Only adjacent boundaries are paired: nested/chattering ambiguous branches + are deliberately left for a richer decoder or human review. + """ + raw = np.asarray(pose, dtype=float) + if raw.ndim != 2 or raw.shape[1] != 7: + raise ValueError('pose must have shape (T, 7)') + cfg = config or BranchConfig() + if cfg.context_frames < 2 or cfg.max_interval_frames < 1: + raise ValueError('context_frames must be >=2 and max_interval_frames >=1') + finite = np.isfinite(raw).all(axis=1) & (np.linalg.norm(raw[:, 3:], axis=1) > 1e-12) + available = finite.copy() + for start, count in known_gaps: + if count > 0: + available[max(0, int(start)):max(0, min(len(raw), int(start)+int(count)))] = False + anchor_ok = available.copy() + if trusted is not None: + trusted = np.asarray(trusted, dtype=bool) + if trusted.shape != (len(raw),): + raise ValueError('trusted must have shape (T,)') + anchor_ok &= trusted + if len(raw) < 2*cfg.context_frames + 1: + return [] + step_rot = np.full(len(raw)-1, np.nan) + pairs = np.flatnonzero(finite[:-1] & finite[1:]) + if len(pairs): + step_rot[pairs] = np.degrees((Rotation.from_quat(raw[pairs+1, 3:])* + Rotation.from_quat(raw[pairs, 3:]).inv()).magnitude()) + step_trans = np.linalg.norm(np.diff(raw[:, :3], axis=0), axis=1)*1000 + edges = np.flatnonzero((step_rot > cfg.rotation_seed_deg) | + ((step_rot > cfg.coupled_rotation_deg) & + (step_trans > cfg.coupled_translation_mm))) + candidates: list[BranchCandidate] = [] + used_until = -1 + for entry, exit_edge in zip(edges[:-1], edges[1:]): + start, end = int(entry+1), int(exit_edge) + left, right = start-cfg.context_frames, end+1+cfg.context_frames + # A return boundary already used to close a proposal cannot also open + # its inverse over the clean interval leading to the next occurrence. + if start <= used_until+1 or left < 0 or right > len(raw): + continue + if end-start+1 > cfg.max_interval_frames or not available[start:end+1].all(): + continue + if not anchor_ok[left:start].all() or not anchor_ok[end+1:right].all(): + continue + # Contexts that cross another jump cannot establish either branch. + context_edges = np.r_[np.arange(left, start-1), np.arange(end+1, right-1)] + if (np.any(step_rot[context_edges] > cfg.coupled_rotation_deg) or + np.any(step_trans[context_edges] > cfg.max_step_translation_mm)): + continue + expected_entry = _predict_next(raw[left:start]) + expected_exit = _predict_next(raw[end+1:right][::-1]) + entry_t, entry_r = _transform(expected_entry, raw[start]) + exit_t, exit_r = _transform(expected_exit, raw[end]) + transform_rot = float(np.degrees((entry_r.inv()*exit_r).magnitude())) + transform_trans = float(np.linalg.norm(entry_t-exit_t)*1000) + if (transform_rot > cfg.transform_rotation_tolerance_deg or + transform_trans > cfg.transform_translation_tolerance_mm): + continue + branch_r = Rotation.from_quat(np.stack([entry_r.as_quat(), exit_r.as_quat()])).mean() + branch_t = (entry_t+exit_t)/2 + corrected_r = Rotation.from_quat(raw[start:end+1, 3:])*branch_r.inv() + corrected_t = raw[start:end+1, :3]-corrected_r.apply(branch_t) + corrected = np.column_stack((corrected_t, corrected_r.as_quat())) + boundary_trans = max(float(np.linalg.norm(corrected_t[i]-pred[0])*1000) + for i, pred in ((0, expected_entry), (-1, expected_exit))) + boundary_rot = max(float(np.degrees((pred[1].inv()*corrected_r[i]).magnitude())) + for i, pred in ((0, expected_entry), (-1, expected_exit))) + joined = np.vstack((raw[start-1], corrected, raw[end+1])) + joined_r = Rotation.from_quat(joined[:, 3:]) + max_rot = float(np.max(np.degrees((joined_r[1:]*joined_r[:-1].inv()).magnitude()))) + max_trans = float(np.max(np.linalg.norm(np.diff(joined[:, :3], axis=0), axis=1)*1000)) + if (boundary_trans > cfg.boundary_translation_tolerance_mm or + boundary_rot > cfg.boundary_rotation_tolerance_deg or + max_rot > cfg.max_step_rotation_deg or + max_trans > cfg.max_step_translation_mm): + continue + candidates.append(BranchCandidate(start, end, corrected, branch_r.as_quat(), branch_t, { + 'method': 'paired_body_branch', + 'duration_frames': end-start+1, + 'bidirectional_transform_agrees': True, + 'transform_rotation_disagreement_deg': transform_rot, + 'transform_translation_disagreement_mm': transform_trans, + 'boundary_prediction_rotation_max_deg': boundary_rot, + 'boundary_prediction_translation_max_mm': boundary_trans, + 'reconstructed_max_step_rotation_deg': max_rot, + 'reconstructed_max_step_translation_mm': max_trans, + 'left_anchor': start-1, + 'right_anchor': end+1, + 'context_frames': cfg.context_frames, + 'independent_anchor_mask_supplied': trusted is not None, + 'physical': True, + 'benchmark_validated': False, + })) + used_until = end + # Support is restricted to this supplied recording/body stream; unrelated + # epochs or rigid bodies must never be pooled implicitly. + for index, candidate in enumerate(candidates): + r = Rotation.from_quat(candidate.branch_rotation_xyzw) + support = 0 + for other in candidates: + rotation_error = np.degrees((r.inv()*Rotation.from_quat(other.branch_rotation_xyzw)).magnitude()) + translation_error = np.linalg.norm(candidate.branch_translation_m-other.branch_translation_m)*1000 + if (rotation_error <= cfg.transform_rotation_tolerance_deg and + translation_error <= cfg.transform_translation_tolerance_mm): + support += 1 + candidates[index] = replace(candidate, evidence={**candidate.evidence, + 'matching_return_pairs': support}) + return candidates diff --git a/action_repair_tools/twm/react_preprocess/mocap_branch_review.py b/action_repair_tools/twm/react_preprocess/mocap_branch_review.py new file mode 100644 index 0000000000000000000000000000000000000000..75a2b15d0e3df5f7adeed9ff4742dc7e0f3077a2 --- /dev/null +++ b/action_repair_tools/twm/react_preprocess/mocap_branch_review.py @@ -0,0 +1,90 @@ +"""Extend review invalidity across plausible persistent alternate branches.""" +from __future__ import annotations + +from dataclasses import asdict, replace +import json + +import numpy as np + +from .mocap_branch import find_branch_candidates +from .mocap_repair import AnomalyBout, Confidence, PoseRepairResult, RepairEvent + + +def apply_loss_evidence_gate(result: PoseRepairResult, raw: np.ndarray, + known_gaps=()) -> PoseRepairResult: + """Require declared loss or invalid raw observations for automatic HIGH.""" + raw = np.asarray(raw, dtype=float) + loss = ~np.isfinite(raw).all(axis=1) | (np.linalg.norm(raw[:, 3:], axis=1) <= 1e-12) + for start, count in known_gaps: + if int(count) > 0: + loss[max(0, int(start)):max(0, min(len(raw), int(start)+int(count)))] = True + confidence, valid = result.confidence.copy(), result.valid.copy() + events = [] + for event in result.events: + if event.confidence == Confidence.HIGH: + rows = np.asarray(event.replaced_frames, dtype=int) + supported = bool(len(rows) and loss[rows].all()) + evidence = {**event.evidence, 'independent_loss_evidence': supported, + 'review_required': not supported} + if not supported: + confidence[event.bout.start:event.bout.end+1] = Confidence.MEDIUM + valid[event.bout.start:event.bout.end+1] = False + event = replace(event, confidence=Confidence.MEDIUM, evidence=evidence) + else: + event = replace(event, evidence=evidence) + events.append(event) + return replace(result, confidence=confidence, valid=valid, events=tuple(events)) + + +def apply_review_branches(result: PoseRepairResult, raw: np.ndarray, side: str, + known_gaps=()) -> PoseRepairResult: + """Merge non-HIGH boundary events and invalidate the complete proposal. + + Existing HIGH events take precedence. Proposals supply candidate values, + never training permission. Any larger overlapping original event remains + invalid in full; its original evidence is retained in the merged event. + """ + pose, repaired = result.pose.copy(), result.repaired.copy() + confidence, valid = result.confidence.copy(), result.valid.copy() + event_ids, events = result.event_id.copy(), list(result.events) + for proposal in find_branch_candidates(raw, known_gaps): + overlapping = [e for e in events if e.bout.side == side and + e.bout.start <= proposal.end+1 and e.bout.end >= proposal.start-1] + if not overlapping or any(e.confidence == Confidence.HIGH for e in overlapping): + continue + lo = min(proposal.start, *(e.bout.start for e in overlapping)) + hi = max(proposal.end, *(e.bout.end for e in overlapping)) + # A merged interval cannot overlap an event that retains a different + # row owner. Expand to a fixed point before checking HIGH protection. + while True: + merged = [e for e in events if e.bout.side == side and e.bout.start <= hi and e.bout.end >= lo] + new_lo = min(lo, *(e.bout.start for e in merged)) + new_hi = max(hi, *(e.bout.end for e in merged)) + if (new_lo, new_hi) == (lo, hi): + break + lo, hi = new_lo, new_hi + if any(e.confidence == Confidence.HIGH for e in merged): + continue + eid = f'{side}:{lo}-{hi}:branch_review' + rows = np.arange(proposal.start, proposal.end+1) + pose[rows] = proposal.pose + repaired[rows] = True + confidence[lo:hi+1] = Confidence.MEDIUM + valid[lo:hi+1] = False + event_ids[lo:hi+1] = eid + seeds = tuple(sorted({row for e in merged for row in e.bout.seed_frames})) + bout = AnomalyBout(side, lo, hi, 'branch_review', seeds, + lo-1 if lo else None, hi+1 if hi+1 < len(raw) else None) + evidence = {**proposal.evidence, + 'duration_frames': hi-lo+1, + 'proposal_start': proposal.start, 'proposal_end': proposal.end, + 'requires_independent_branch_identity_evidence': True, + 'training_valid': False, + 'superseded_events_json': json.dumps([asdict(e) for e in merged], + default=lambda value: value.item())} + replacement = RepairEvent(eid, bout, Confidence.MEDIUM, 'paired_body_branch_review', + tuple(np.flatnonzero(repaired[lo:hi+1])+lo), evidence) + removed_ids = {e.event_id for e in merged} + events = [e for e in events if e.event_id not in removed_ids] + [replacement] + return PoseRepairResult(pose, repaired, confidence, valid, event_ids, + tuple(sorted(events, key=lambda e: e.bout.start))) diff --git a/action_repair_tools/twm/react_preprocess/mocap_candidate.py b/action_repair_tools/twm/react_preprocess/mocap_candidate.py new file mode 100644 index 0000000000000000000000000000000000000000..5208454a632ffd81bb16681342cffed394b08411 --- /dev/null +++ b/action_repair_tools/twm/react_preprocess/mocap_candidate.py @@ -0,0 +1,740 @@ +"""Immutable-input writer for mocap repair candidate trees. + +Production release trees are read-only inputs. Every build is pinned to a +manifest of exact parquet digests and writes augmented metadata plus action +sidecars beneath a separate review root. +""" +from __future__ import annotations + +from dataclasses import asdict, dataclass +from datetime import datetime, timezone +import hashlib +import json +import os +from pathlib import Path +import tempfile +from typing import Any, Iterable + +import numpy as np +import pyarrow as pa +import pyarrow.parquet as pq + +from .mocap_repair import Confidence, TaskGate, repair_pose_stream +from .repaired_actions import ActionSeries, actions_fps15, native_actions + + +SIDES = ("left", "right") +MANIFEST_SCHEMA_VERSION = 1 + + +class SourceChangedError(RuntimeError): + """A manifest-pinned input no longer has its recorded content.""" + + +class CandidateVerificationError(RuntimeError): + """Candidate provenance or derived actions disagree with their contract.""" + + +@dataclass(frozen=True) +class ManifestFile: + path: str + bytes: int + rows: int + mtime_ns: int + sha256: str + + +@dataclass(frozen=True) +class InputManifest: + source_root: str + tasks: tuple[str, ...] + files: tuple[ManifestFile, ...] + created_utc: str + digest: str + + def to_dict(self) -> dict[str, Any]: + return { + "schema_version": MANIFEST_SCHEMA_VERSION, + "source_root": self.source_root, + "tasks": list(self.tasks), + "created_utc": self.created_utc, + "digest": self.digest, + "files": [asdict(item) for item in self.files], + } + + @classmethod + def from_dict(cls, value: dict[str, Any]) -> "InputManifest": + if int(value.get("schema_version", -1)) != MANIFEST_SCHEMA_VERSION: + raise ValueError("unsupported input manifest schema") + return cls( + source_root=str(value["source_root"]), + tasks=tuple(map(str, value["tasks"])), + files=tuple(ManifestFile(**item) for item in value["files"]), + created_utc=str(value["created_utc"]), + digest=str(value["digest"]), + ) + + +@dataclass(frozen=True) +class CandidateEpisode: + task: str + date: str + episode: str + parquet: Path + events: Path + repaired_frames: int + + +@dataclass(frozen=True) +class CandidateBuild: + root: Path + manifest_digest: str + episodes: tuple[CandidateEpisode, ...] + + +@dataclass(frozen=True) +class VerificationReport: + episodes: int + errors: tuple[str, ...] + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def _canonical_json(value: Any) -> bytes: + return json.dumps(value, sort_keys=True, separators=(",", ":"), + ensure_ascii=True).encode("utf-8") + + +def _manifest_identity(source_root: Path, tasks: tuple[str, ...], + files: tuple[ManifestFile, ...]) -> dict[str, Any]: + return { + "schema_version": MANIFEST_SCHEMA_VERSION, + "source_root": str(source_root), + "tasks": list(tasks), + "files": [{ + "path": item.path, + "bytes": item.bytes, + "rows": item.rows, + "sha256": item.sha256, + } for item in files], + } + + +def _validate_manifest(manifest: InputManifest) -> None: + identity = _manifest_identity( + Path(manifest.source_root).resolve(), manifest.tasks, manifest.files) + expected = hashlib.sha256(_canonical_json(identity)).hexdigest() + if manifest.digest != expected: + raise ValueError( + f"input manifest digest is invalid: {manifest.digest} != {expected}") + + +def snapshot_inputs(source_root: Path, + tasks: Iterable[str]) -> InputManifest: + """Snapshot the exact parquet set used by one candidate run.""" + source_root = Path(source_root).resolve() + task_tuple = tuple(sorted(dict.fromkeys(str(task) for task in tasks))) + files: list[ManifestFile] = [] + for task in task_tuple: + for path in sorted((source_root / task / "meta").glob("*/*.parquet")): + stat = path.stat() + files.append(ManifestFile( + path=path.relative_to(source_root).as_posix(), + bytes=stat.st_size, + rows=pq.ParquetFile(path).metadata.num_rows, + mtime_ns=stat.st_mtime_ns, + sha256=_sha256(path), + )) + files_tuple = tuple(sorted(files, key=lambda item: item.path)) + identity = _manifest_identity(source_root, task_tuple, files_tuple) + digest = hashlib.sha256(_canonical_json(identity)).hexdigest() + return InputManifest( + source_root=str(source_root), + tasks=task_tuple, + files=files_tuple, + created_utc=datetime.now(timezone.utc).isoformat(), + digest=digest, + ) + + +def save_manifest(manifest: InputManifest, path: Path) -> Path: + """Persist a manifest, refusing to replace a different snapshot.""" + _validate_manifest(manifest) + path = Path(path) + if path.is_file(): + prior = InputManifest.from_dict(json.loads(path.read_text())) + _validate_manifest(prior) + if prior.digest != manifest.digest: + raise ValueError( + f"manifest path already contains a different snapshot: {path}") + _atomic_json(path, manifest.to_dict()) + return path + + +def load_manifest(path: Path) -> InputManifest: + manifest = InputManifest.from_dict(json.loads(Path(path).read_text())) + _validate_manifest(manifest) + return manifest + + +def _atomic_json(path: Path, value: Any) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile( + mode="w", encoding="utf-8", dir=path.parent, + prefix=f".{path.name}.", suffix=".tmp", delete=False) as stream: + temp = Path(stream.name) + json.dump(value, stream, indent=2, sort_keys=True) + stream.write("\n") + os.replace(temp, path) + + +def _is_within(path: Path, root: Path) -> bool: + return path == root or root in path.parents + + +def _pose_list_array(value: np.ndarray) -> pa.Array: + return pa.array(np.asarray(value, dtype=np.float64).tolist(), + type=pa.list_(pa.float64())) + + +def _set_column(table: pa.Table, name: str, value: pa.Array) -> pa.Table: + if name in table.column_names: + return table.set_column(table.schema.get_field_index(name), name, value) + return table.append_column(name, value) + + +def _jsonable(value: Any) -> Any: + if isinstance(value, np.generic): + return value.item() + if isinstance(value, dict): + return {str(key): _jsonable(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [_jsonable(item) for item in value] + return value + + +def _event_dict(event: Any) -> dict[str, Any]: + return _jsonable({ + "event_id": event.event_id, + "side": event.bout.side, + "start": event.bout.start, + "end": event.bout.end, + "kind": event.bout.kind, + "seed_frames": list(event.bout.seed_frames), + "left_context_end": event.bout.left_context_end, + "right_context_start": event.bout.right_context_start, + "confidence": Confidence(event.confidence).name, + "method": event.method, + "replaced_frames": list(event.replaced_frames), + "evidence": event.evidence, + }) + + +def _write_actions(path: Path, actions: ActionSeries) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + event_text = np.asarray(["|".join(ids) for ids in actions.event_ids], + dtype=np.str_) + with tempfile.NamedTemporaryFile( + mode="wb", dir=path.parent, prefix=f".{path.name}.", + suffix=".tmp", delete=False) as stream: + temp = Path(stream.name) + np.savez_compressed( + stream, + values=np.asarray(actions.values, np.float32), + valid=np.asarray(actions.valid, bool), + repaired=np.asarray(actions.repaired, bool), + event_ids=event_text, + start_rows=np.asarray(actions.start_rows, np.int32), + end_rows=np.asarray(actions.end_rows, np.int32), + rotation_deg=np.asarray(actions.rotation_deg, np.float32), + translation_mm=np.asarray(actions.translation_mm, np.float32), + ) + os.replace(temp, path) + + +class CandidateWriter: + """Build candidate metadata pinned to one immutable input manifest.""" + + def __init__(self, source_root: Path, output_root: Path, + manifest: InputManifest, *, + task_gates: dict[str, TaskGate] | None = None, + production_roots: Iterable[Path] = (), + branch_review: bool = False, + require_loss_evidence: bool = False): + self.source_root = Path(source_root).resolve() + self.output_root = Path(output_root).resolve() + self.manifest = manifest + _validate_manifest(manifest) + self.task_gates = dict(task_gates or {}) + self.branch_review = bool(branch_review) + self.require_loss_evidence = bool(require_loss_evidence) + inferred = { + self.source_root, + self.source_root.parent / "release_zup", + self.source_root.parent / "release_cut", + } + inferred.update(Path(path).resolve() for path in production_roots) + for root in inferred: + if (_is_within(self.output_root, root) + or _is_within(root, self.output_root)): + raise ValueError( + f"candidate output overlaps production root: " + f"{self.output_root} vs {root}") + if Path(manifest.source_root).resolve() != self.source_root: + raise ValueError("manifest source_root does not match writer source") + + def _verify_source(self, item: ManifestFile) -> Path: + path = self.source_root / item.path + if not path.is_file(): + raise SourceChangedError(f"source missing after manifest: {item.path}") + stat = path.stat() + actual = _sha256(path) + if actual != item.sha256: + raise SourceChangedError( + f"source digest changed after manifest: {item.path} " + f"(bytes {item.bytes} -> {stat.st_size})") + rows = pq.ParquetFile(path).metadata.num_rows + if rows != item.rows: + raise SourceChangedError( + f"source row count changed after manifest: {item.path}") + return path + + def _known_gaps(self, task: str, date: str, + episode: str) -> dict[str, list[tuple[int, int]]]: + path = self.source_root / task / "pose_gaps.json" + if not path.is_file(): + return {} + payload = json.loads(path.read_text()) + row = payload.get(f"{date}/{episode}", {}) + return {side: [tuple(map(int, gap)) for gap in row.get(side, [])] + for side in SIDES} + + def _write_parquet_atomic(self, table: pa.Table, output: Path) -> None: + output.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile( + dir=output.parent, prefix=f".{output.name}.", + suffix=".tmp", delete=False) as stream: + temp = Path(stream.name) + try: + pq.write_table(table, temp, compression="zstd") + os.replace(temp, output) + finally: + if temp.exists(): + temp.unlink() + + def _write_episode(self, item: ManifestFile) -> CandidateEpisode: + source = self._verify_source(item) + rel = Path(item.path) + task, marker, date, filename = rel.parts + if marker != "meta": + raise ValueError(f"manifest path is not task/meta/date: {item.path}") + episode = Path(filename).stem + table = pq.read_table(source) + output_table = table + gaps = self._known_gaps(task, date, episode) + events: list[dict[str, Any]] = [] + repaired_total = 0 + gate = self.task_gates.get(task, TaskGate()) + for side in SIDES: + pose_name = f"sensor_{side}_pose" + if pose_name not in table.column_names: + continue + raw_pose = np.asarray(table[pose_name].to_pylist(), dtype=float) + result = repair_pose_stream( + raw_pose, side, gaps.get(side, ()), task_gate=gate) + if self.require_loss_evidence: + from .mocap_branch_review import apply_loss_evidence_gate + result = apply_loss_evidence_gate(result, raw_pose, gaps.get(side, ())) + if self.branch_review: + from .mocap_branch_review import apply_review_branches + result = apply_review_branches(result, raw_pose, side, gaps.get(side, ())) + output_table = _set_column( + output_table, pose_name, _pose_list_array(result.pose)) + output_table = _set_column( + output_table, f"pose_{side}_repaired", + pa.array(result.repaired, type=pa.bool_())) + output_table = _set_column( + output_table, f"pose_{side}_repair_confidence", + pa.array(result.confidence, type=pa.uint8())) + output_table = _set_column( + output_table, f"pose_{side}_valid", + pa.array(result.valid, type=pa.bool_())) + output_table = _set_column( + output_table, f"pose_{side}_repair_event_id", + pa.array(result.event_id.astype(str), type=pa.string())) + repaired_total += int(result.repaired.sum()) + events.extend(_event_dict(event) for event in result.events) + native = native_actions( + result.pose, result.valid, result.repaired, result.event_id) + half = actions_fps15(result.pose, native) + _write_actions( + self.output_root / task / "actions_native" / date + / f"{episode}_{side}.npz", native) + _write_actions( + self.output_root / task / "actions_fps15" / date + / f"{episode}_{side}.npz", half) + + output_table = output_table.replace_schema_metadata( + dict(table.schema.metadata or {})) + output = self.output_root / item.path + self._write_parquet_atomic(output_table, output) + event_path = (self.output_root / task / "repair_events" / date + / f"{episode}.json") + _atomic_json(event_path, { + "schema_version": 1, + "manifest_digest": self.manifest.digest, + "source_path": item.path, + "source_sha256": item.sha256, + "task": task, + "date": date, + "episode": episode, + "events": sorted(events, key=lambda event: event["event_id"]), + }) + return CandidateEpisode( + task, date, episode, output, event_path, repaired_total) + + def write_all(self) -> CandidateBuild: + """Verify the complete snapshot, then build only its declared files.""" + for item in self.manifest.files: + self._verify_source(item) + manifest_path = self.output_root / "input_manifest.json" + if manifest_path.exists(): + prior = json.loads(manifest_path.read_text()) + if prior.get("digest") != self.manifest.digest: + raise ValueError( + "candidate root contains a different input manifest digest") + _atomic_json(manifest_path, self.manifest.to_dict()) + _atomic_json(self.output_root / "build_config.json", { + "schema_version": 1, + "manifest_digest": self.manifest.digest, + "branch_review": self.branch_review, + "require_loss_evidence": self.require_loss_evidence, + "task_gates": { + task: asdict(self.task_gates.get(task, TaskGate())) + for task in self.manifest.tasks + }, + }) + episodes = tuple(self._write_episode(item) for item in self.manifest.files) + _atomic_json(self.output_root / "build_summary.json", { + "schema_version": 1, + "manifest_digest": self.manifest.digest, + "episodes": [{ + "task": episode.task, + "date": episode.date, + "episode": episode.episode, + "parquet": episode.parquet.relative_to(self.output_root).as_posix(), + "events": episode.events.relative_to(self.output_root).as_posix(), + "repaired_frames": episode.repaired_frames, + } for episode in episodes], + }) + return CandidateBuild(self.output_root, self.manifest.digest, episodes) + + +def _load_candidate(candidate_root: Path) -> tuple[InputManifest, dict[str, TaskGate]]: + candidate_root = Path(candidate_root).resolve() + manifest_path = candidate_root / "input_manifest.json" + config_path = candidate_root / "build_config.json" + if not manifest_path.is_file() or not config_path.is_file(): + raise ValueError("candidate root is missing manifest or build config") + manifest = InputManifest.from_dict(json.loads(manifest_path.read_text())) + config = json.loads(config_path.read_text()) + if config.get("manifest_digest") != manifest.digest: + raise ValueError("build config manifest digest mismatch") + gates = {task: TaskGate(**value) + for task, value in config.get("task_gates", {}).items()} + return manifest, gates + + +def _scoped_event_id(path: Path, event_id: str) -> str: + return f"{path.parent.parent.parent.name}/{path.parent.name}/{path.stem}::{event_id}" + + +def _event_index(candidate_root: Path) -> dict[str, tuple[Path, dict[str, Any]]]: + """Index review IDs as task/date/source_episode::local_event_id. + + Legacy local IDs are accepted only when globally unique. Exported samples + keyed remote_path::local_event_id must first be mapped through publication + inventory to the unsegmented source episode; segmented filenames are not + sufficient to infer this mapping. + """ + index: dict[str, tuple[Path, dict[str, Any]]] = {} + aliases: dict[str, list[tuple[Path, dict[str, Any]]]] = {} + for path in sorted(Path(candidate_root).glob("*/repair_events/*/*.json")): + payload = json.loads(path.read_text()) + for event in payload.get("events", []): + event_id = str(event["event_id"]) + scoped = _scoped_event_id(path, event_id) + if scoped in index: + raise ValueError(f"duplicate event within episode: {scoped}") + index[scoped] = (path, event) + aliases.setdefault(event_id, []).append((path, event)) + index.update({alias: values[0] for alias, values in aliases.items() if len(values) == 1}) + return index + + +def _canonical_decisions(payload: dict[str, Any], + manifest: InputManifest, + events: dict[str, tuple[Path, dict[str, Any]]]) -> list[dict[str, str]]: + if int(payload.get("schema_version", -1)) != 1: + raise ValueError("unsupported decisions schema") + if payload.get("manifest_digest") != manifest.digest: + raise ValueError("decisions manifest digest mismatch") + allowed = {"keep_raw", "accept_repair", "invalidate", "unsure"} + seen: set[str] = set() + out = [] + for row in payload.get("decisions", []): + event_id = str(row.get("event_id", "")) + decision = str(row.get("decision", "")) + if event_id not in events: + raise ValueError(f"unknown event in decisions: {event_id}") + path, event = events[event_id] + event_id = _scoped_event_id(path, str(event["event_id"])) + if event_id in seen: + raise ValueError(f"duplicate decision for event: {event_id}") + if decision not in allowed: + raise ValueError(f"invalid decision {decision!r} for {event_id}") + seen.add(event_id) + out.append({"event_id": event_id, "decision": decision}) + return sorted(out, key=lambda row: row["event_id"]) + + +def _finite_unit_pose(pose: np.ndarray) -> np.ndarray: + norm = np.linalg.norm(pose[:, 3:], axis=1) + return np.isfinite(pose).all(axis=1) & (norm > 1e-12) + + +def apply_decisions(candidate_root: Path, + payload: dict[str, Any]) -> CandidateBuild: + """Replay canonical review decisions from the immutable source snapshot.""" + candidate_root = Path(candidate_root).resolve() + manifest, gates = _load_candidate(candidate_root) + events_before = _event_index(candidate_root) + decisions = _canonical_decisions(payload, manifest, events_before) + + # Rebuild the undecided baseline first. This makes decision application + # independent of whatever decision was applied on the previous run. + writer = CandidateWriter( + Path(manifest.source_root), candidate_root, manifest, + task_gates=gates, + branch_review=bool(json.loads((candidate_root / "build_config.json").read_text()).get("branch_review", False)), + require_loss_evidence=bool(json.loads((candidate_root / "build_config.json").read_text()).get("require_loss_evidence", False))) + build = writer.write_all() + events = _event_index(candidate_root) + grouped: dict[Path, list[dict[str, str]]] = {} + for row in decisions: + path, event = events[row["event_id"]] + grouped.setdefault(path, []).append({"event_id": str(event["event_id"]), + "decision": row["decision"]}) + + for event_path, rows in grouped.items(): + event_payload = json.loads(event_path.read_text()) + by_id = {event["event_id"]: event + for event in event_payload.get("events", [])} + task = str(event_payload["task"]) + date = str(event_payload["date"]) + episode = str(event_payload["episode"]) + candidate_path = candidate_root / task / "meta" / date / f"{episode}.parquet" + source_path = Path(manifest.source_root) / event_payload["source_path"] + table = pq.read_table(candidate_path) + source = pq.read_table(source_path) + side_state: dict[str, dict[str, np.ndarray]] = {} + for side in SIDES: + pose_name = f"sensor_{side}_pose" + if pose_name not in table.column_names: + continue + side_state[side] = { + "pose": np.asarray(table[pose_name].to_pylist(), float), + "raw": np.asarray(source[pose_name].to_pylist(), float), + "repaired": np.asarray( + table[f"pose_{side}_repaired"], bool).copy(), + "confidence": np.asarray( + table[f"pose_{side}_repair_confidence"], np.uint8).copy(), + "valid": np.asarray( + table[f"pose_{side}_valid"], bool).copy(), + "event_id": np.asarray( + table[f"pose_{side}_repair_event_id"].to_pylist(), object), + } + for row in rows: + event = by_id[row["event_id"]] + side = str(event["side"]) + state = side_state[side] + selected = np.arange(int(event["start"]), int(event["end"]) + 1) + decision = row["decision"] + if decision == "accept_repair": + state["valid"][selected] = _finite_unit_pose(state["pose"][selected]) + else: + state["pose"][selected] = state["raw"][selected] + state["repaired"][selected] = False + if decision == "keep_raw": + state["confidence"][selected] = Confidence.NONE + state["valid"][selected] = _finite_unit_pose(state["raw"][selected]) + elif decision == "invalidate": + state["confidence"][selected] = Confidence.LOW + state["valid"][selected] = False + else: # unsure + state["valid"][selected] = False + event["decision"] = decision + + output_table = table + for side, state in side_state.items(): + output_table = _set_column( + output_table, f"sensor_{side}_pose", _pose_list_array(state["pose"])) + output_table = _set_column( + output_table, f"pose_{side}_repaired", + pa.array(state["repaired"], type=pa.bool_())) + output_table = _set_column( + output_table, f"pose_{side}_repair_confidence", + pa.array(state["confidence"], type=pa.uint8())) + output_table = _set_column( + output_table, f"pose_{side}_valid", + pa.array(state["valid"], type=pa.bool_())) + native = native_actions( + state["pose"], state["valid"], state["repaired"], + state["event_id"]) + half = actions_fps15(state["pose"], native) + _write_actions( + candidate_root / task / "actions_native" / date + / f"{episode}_{side}.npz", native) + _write_actions( + candidate_root / task / "actions_fps15" / date + / f"{episode}_{side}.npz", half) + writer._write_parquet_atomic(output_table, candidate_path) + _atomic_json(event_path, event_payload) + + _atomic_json(candidate_root / "decisions.json", { + "schema_version": 1, + "manifest_digest": manifest.digest, + "decisions": decisions, + }) + return build + + +def _rows_equal(left: np.ndarray, right: np.ndarray) -> np.ndarray: + same = left == right + if np.issubdtype(left.dtype, np.floating): + same |= np.isnan(left) & np.isnan(right) + return np.all(same, axis=1) + + +def _columns_equal(left: pa.ChunkedArray, + right: pa.ChunkedArray) -> bool: + """Arrow equality with IEEE NaNs treated as equal at identical positions.""" + if left.type != right.type or len(left) != len(right): + return False + if left.equals(right): + return True + try: + if (pa.types.is_list(left.type) + or pa.types.is_large_list(left.type) + or pa.types.is_fixed_size_list(left.type)): + a = np.asarray(left.to_pylist(), dtype=float) + b = np.asarray(right.to_pylist(), dtype=float) + elif pa.types.is_floating(left.type): + a = np.asarray(left.to_numpy(zero_copy_only=False), dtype=float) + b = np.asarray(right.to_numpy(zero_copy_only=False), dtype=float) + else: + return False + except (TypeError, ValueError): + return False + return bool(np.array_equal(a, b, equal_nan=True)) + + +def _verify_action_file(path: Path, expected: ActionSeries, + errors: list[str]) -> None: + if not path.is_file(): + errors.append(f"missing action sidecar: {path}") + return + try: + with np.load(path, allow_pickle=False) as data: + for name, value in ( + ("values", expected.values), ("valid", expected.valid), + ("repaired", expected.repaired), + ("start_rows", expected.start_rows), + ("end_rows", expected.end_rows)): + if name not in data.files or not np.array_equal(data[name], value): + errors.append(f"action mismatch {path}: {name}") + event_text = np.asarray(["|".join(ids) for ids in expected.event_ids], + dtype=np.str_) + if "event_ids" not in data.files or not np.array_equal( + data["event_ids"], event_text): + errors.append(f"action mismatch {path}: event_ids") + except Exception as exc: + errors.append(f"unreadable action sidecar {path}: {exc}") + + +def verify_candidate(candidate_root: Path) -> VerificationReport: + """Verify source immutability, declared changes, and action derivations.""" + candidate_root = Path(candidate_root).resolve() + manifest, gates = _load_candidate(candidate_root) + writer = CandidateWriter( + Path(manifest.source_root), candidate_root, manifest, + task_gates=gates) + errors: list[str] = [] + for item in manifest.files: + try: + source_path = writer._verify_source(item) + except SourceChangedError as exc: + errors.append(str(exc)); continue + candidate_path = candidate_root / item.path + if not candidate_path.is_file(): + errors.append(f"missing candidate parquet: {item.path}"); continue + try: + source = pq.read_table(source_path) + candidate = pq.read_table(candidate_path) + except Exception as exc: + errors.append(f"unreadable candidate {item.path}: {exc}"); continue + if source.num_rows != candidate.num_rows: + errors.append(f"row count mismatch: {item.path}"); continue + for name in source.column_names: + if name.startswith("sensor_") and name.endswith("_pose"): + continue + if (name not in candidate.column_names + or not _columns_equal(source[name], candidate[name])): + errors.append(f"source column changed: {item.path}:{name}") + rel = Path(item.path) + task, _, date, filename = rel.parts + episode = Path(filename).stem + for side in SIDES: + pose_name = f"sensor_{side}_pose" + required = [pose_name, f"pose_{side}_repaired", + f"pose_{side}_repair_confidence", f"pose_{side}_valid", + f"pose_{side}_repair_event_id"] + if pose_name not in source.column_names: + continue + if any(name not in candidate.column_names for name in required): + errors.append(f"missing repair columns: {item.path}:{side}"); continue + raw = np.asarray(source[pose_name].to_pylist(), float) + pose = np.asarray(candidate[pose_name].to_pylist(), float) + repaired = np.asarray(candidate[f"pose_{side}_repaired"], bool) + valid = np.asarray(candidate[f"pose_{side}_valid"], bool) + event_id = np.asarray( + candidate[f"pose_{side}_repair_event_id"].to_pylist(), object) + changed = ~_rows_equal(raw, pose) + if np.any(changed & ~repaired): + errors.append(f"undeclared pose change: {item.path}:{side}") + if np.any(repaired & (event_id == "")): + errors.append(f"repaired pose missing event id: {item.path}:{side}") + finite_unit = _finite_unit_pose(pose) + if np.any(valid & ~finite_unit): + errors.append(f"valid pose is nonfinite/nonunit: {item.path}:{side}") + norm = np.linalg.norm(pose[valid, 3:], axis=1) + if len(norm) and not np.allclose(norm, 1.0, atol=1e-6): + errors.append(f"valid quaternion is not unit: {item.path}:{side}") + native = native_actions(pose, valid, repaired, event_id) + half = actions_fps15(pose, native) + _verify_action_file( + candidate_root / task / "actions_native" / date + / f"{episode}_{side}.npz", native, errors) + _verify_action_file( + candidate_root / task / "actions_fps15" / date + / f"{episode}_{side}.npz", half, errors) + report = VerificationReport(len(manifest.files), tuple(errors)) + if errors: + raise CandidateVerificationError("; ".join(errors)) + return report diff --git a/action_repair_tools/twm/react_preprocess/mocap_repair.py b/action_repair_tools/twm/react_preprocess/mocap_repair.py new file mode 100644 index 0000000000000000000000000000000000000000..83fed063b81103a650e609c71aea280a060c5e67 --- /dev/null +++ b/action_repair_tools/twm/react_preprocess/mocap_repair.py @@ -0,0 +1,624 @@ +"""Conservative, provenance-preserving OptiTrack trajectory repair. + +This module starts by grouping discontinuity seeds into complete tracking-loss +bouts. Detection is deliberately separate from reconstruction: a surprising +edge is evidence to inspect, never by itself permission to replace a pose. +""" +from __future__ import annotations + +from dataclasses import dataclass +from enum import IntEnum +from typing import Iterable + +import numpy as np +from scipy.spatial.transform import Rotation, Slerp + + +@dataclass(frozen=True) +class RepairConfig: + """Physical and temporal gates expressed at the native 30 fps rate.""" + + rotation_seed_deg: float = 30.0 + coupled_rotation_deg: float = 12.0 + coupled_translation_mm: float = 20.0 + branch_agreement_deg: float = 12.0 + branch_context_frames: int = 15 + stable_frames: int = 10 + context_frames: int = 15 + translation_inlier_mm: float = 20.0 + rotation_inlier_deg: float = 12.0 + max_high_frames: int = 60 + max_step_translation_mm: float = 50.0 + max_step_rotation_deg: float = 30.0 + max_prediction_translation_mm: float = 10.0 + max_prediction_rotation_deg: float = 5.0 + + +@dataclass(frozen=True) +class AnomalyBout: + """One complete interval requiring reconstruction or review.""" + + side: str + start: int + end: int + kind: str + seed_frames: tuple[int, ...] + left_context_end: int | None + right_context_start: int | None + + +class Confidence(IntEnum): + NONE = 0 + LOW = 1 + MEDIUM = 2 + HIGH = 3 + + +@dataclass(frozen=True) +class TaskGate: + """Held-out clean-data evidence allowed to enable automatic training.""" + + high_confidence_enabled: bool = False + validated_max_frames: int = 0 + endpoint_max_frames: int = 0 + + +@dataclass(frozen=True) +class RepairEvent: + event_id: str + bout: AnomalyBout + confidence: Confidence + method: str + replaced_frames: tuple[int, ...] + evidence: dict[str, float | int | bool | str] + + +@dataclass(frozen=True) +class PoseRepairResult: + pose: np.ndarray + repaired: np.ndarray + confidence: np.ndarray + valid: np.ndarray + event_id: np.ndarray + events: tuple[RepairEvent, ...] + + +def _unit_quaternion(q: np.ndarray) -> np.ndarray: + norm = np.linalg.norm(q, axis=-1, keepdims=True) + return q / np.where(norm > 1e-12, norm, 1.0) + + +def _quaternion_angle_deg(a: np.ndarray, b: np.ndarray) -> np.ndarray: + a, b = _unit_quaternion(a), _unit_quaternion(b) + dot = np.abs(np.sum(a * b, axis=-1)) + return np.degrees(2.0 * np.arccos(np.clip(dot, -1.0, 1.0))) + + +def transition_metrics(pose: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """Return sign-insensitive rotation degrees and translation millimetres.""" + pose = np.asarray(pose, dtype=float) + if pose.ndim != 2 or pose.shape[1] != 7: + raise ValueError(f"pose must have shape (T, 7), got {pose.shape}") + if len(pose) < 2: + return np.empty(0, dtype=float), np.empty(0, dtype=float) + finite = np.isfinite(pose).all(axis=1) + rot = _quaternion_angle_deg(pose[:-1, 3:], pose[1:, 3:]) + trans = np.linalg.norm(np.diff(pose[:, :3], axis=0), axis=1) * 1000.0 + invalid = ~(finite[:-1] & finite[1:]) + rot[invalid] = np.nan + trans[invalid] = np.nan + return rot, trans + + +def _branch_support(pose: np.ndarray, rows: set[int], config: RepairConfig) -> dict[int, float]: + """Fraction of nearby finite observations agreeing with each candidate row.""" + q = np.asarray(pose, dtype=float)[:, 3:] + finite = np.isfinite(q).all(axis=1) & (np.linalg.norm(q, axis=1) > 1e-12) + out: dict[int, float] = {} + for row in sorted(rows): + if not finite[row]: + out[row] = -1.0 + continue + lo = max(0, row - config.branch_context_frames) + hi = min(len(q), row + config.branch_context_frames + 1) + neighbours = np.arange(lo, hi) + neighbours = neighbours[(neighbours != row) & finite[lo:hi]] + if not len(neighbours): + out[row] = 1.0 + continue + angle = _quaternion_angle_deg( + np.repeat(q[row][None], len(neighbours), axis=0), q[neighbours]) + out[row] = float(np.mean(angle <= config.branch_agreement_deg)) + return out + + +def _discontinuity_seed_frames(pose: np.ndarray, config: RepairConfig) -> set[int]: + rot, trans = transition_metrics(pose) + edges = np.where( + (rot > config.rotation_seed_deg) + | ((rot > config.coupled_rotation_deg) + & (trans > config.coupled_translation_mm)) + )[0] + if not len(edges): + return set() + endpoints = {int(i) for edge in edges for i in (edge, edge + 1)} + support = _branch_support(pose, endpoints, config) + seeds: set[int] = set() + for edge in edges: + left, right = int(edge), int(edge + 1) + ls, rs = support[left], support[right] + # The alternate OptiTrack branch has less local temporal support. A + # tie remains a review seed on the post-transition side; reconstruction + # later requires two trustworthy anchors before changing anything. + seeds.add(left if ls < rs else right) + return seeds + + +def detect_bouts( + pose: np.ndarray, + side: str, + known_gaps: Iterable[tuple[int, int]] = (), + *, + config: RepairConfig | None = None, +) -> list[AnomalyBout]: + """Group discontinuities and declared gaps into complete anomaly bouts. + + Seeds separated by fewer than ``stable_frames`` clean native frames are + one bout. Consequently a returning A/B flicker is represented once rather + than as a list of unrelated >30 degree transitions. + """ + pose = np.asarray(pose, dtype=float) + if pose.ndim != 2 or pose.shape[1] != 7: + raise ValueError(f"pose must have shape (T, 7), got {pose.shape}") + if side not in {"left", "right"}: + raise ValueError(f"side must be left or right, got {side!r}") + cfg = config or RepairConfig() + n_rows = len(pose) + known: set[int] = set() + for start, count in known_gaps: + start, count = int(start), int(count) + if count > 0: + known.update(range(max(0, start), min(n_rows, start + count))) + nonfinite = set(np.where(~np.isfinite(pose).all(axis=1))[0].astype(int)) + discontinuity = _discontinuity_seed_frames(pose, cfg) + # A declared gap already owns its entry and return boundaries. Counting + # the first clean row after it as another seed makes an N-row gap appear + # to be N+1 rows and can incorrectly cross a confidence-duration gate. + gap_boundaries: set[int] = set() + for start, count in known_gaps: + gap_boundaries.update(range(max(0, int(start) - 1), + min(n_rows, int(start) + int(count) + 1))) + discontinuity -= gap_boundaries + seeds = sorted(known | nonfinite | discontinuity) + if not seeds: + return [] + + groups: list[list[int]] = [[seeds[0]]] + for row in seeds[1:]: + if row - groups[-1][-1] <= cfg.stable_frames: + groups[-1].append(row) + else: + groups.append([row]) + + bouts: list[AnomalyBout] = [] + for group in groups: + start, end = group[0], group[-1] + group_set = set(group) + if group_set <= known and group_set: + kind = "known_gap" + elif group_set & known: + kind = "gap_with_discontinuity" + elif group_set & nonfinite: + kind = "nonfinite" + else: + kind = "branch_discontinuity" + left = start - 1 if start > 0 else None + right = end + 1 if end + 1 < n_rows else None + bouts.append(AnomalyBout( + side=side, + start=start, + end=end, + kind=kind, + seed_frames=tuple(group), + left_context_end=left, + right_context_start=right, + )) + return bouts + + +def _finite_pose_rows(pose: np.ndarray) -> np.ndarray: + q_norm = np.linalg.norm(pose[:, 3:], axis=1) + return np.isfinite(pose).all(axis=1) & (q_norm > 1e-12) + + +def _context_rows(finite: np.ndarray, start: int, end: int, + count: int) -> tuple[np.ndarray, np.ndarray]: + left = np.flatnonzero(finite[:start])[-count:] + right = np.flatnonzero(finite[end + 1:])[:count] + end + 1 + return left, right + + +def _median_linear_velocity(xyz: np.ndarray, rows: np.ndarray) -> np.ndarray: + if len(rows) < 2: + return np.zeros(3, dtype=float) + consecutive = np.diff(rows) == 1 + delta = np.diff(xyz[rows], axis=0)[consecutive] + return np.median(delta, axis=0) if len(delta) else np.zeros(3, dtype=float) + + +def _median_angular_velocity(quat: np.ndarray, rows: np.ndarray) -> np.ndarray: + if len(rows) < 2: + return np.zeros(3, dtype=float) + pairs = np.flatnonzero(np.diff(rows) == 1) + if not len(pairs): + return np.zeros(3, dtype=float) + first = Rotation.from_quat(quat[rows[pairs]]) + second = Rotation.from_quat(quat[rows[pairs + 1]]) + return np.median((second * first.inv()).as_rotvec(), axis=0) + + +def _hermite(p0: np.ndarray, p1: np.ndarray, v0: np.ndarray, + v1: np.ndarray, duration: int, offsets: np.ndarray) -> np.ndarray: + s = (offsets / float(duration))[:, None] + h00 = 2 * s**3 - 3 * s**2 + 1 + h10 = s**3 - 2 * s**2 + s + h01 = -2 * s**3 + 3 * s**2 + h11 = s**3 - s**2 + return h00 * p0 + h10 * duration * v0 + h01 * p1 + h11 * duration * v1 + + +def _rotation_error_deg(a: np.ndarray, b: np.ndarray) -> np.ndarray: + return np.degrees( + (Rotation.from_quat(a) * Rotation.from_quat(b).inv()).magnitude()) + + +def _predict_bout(pose: np.ndarray, bout: AnomalyBout, left: np.ndarray, + right: np.ndarray) -> tuple[np.ndarray, dict[str, float]]: + """Boundary-constrained SE(3) prediction and independent check evidence.""" + lo, hi = int(left[-1]), int(right[0]) + rows = np.arange(bout.start, bout.end + 1) + duration = hi - lo + offsets = rows - lo + xyz = pose[:, :3] + quat = _unit_quaternion(pose[:, 3:]) + v0 = _median_linear_velocity(xyz, left) + v1 = _median_linear_velocity(xyz, right) + # Both effectively stationary: the minimum-assumption path is linear. + if max(np.linalg.norm(v0), np.linalg.norm(v1)) < 0.0005: + v0 = v1 = (xyz[hi] - xyz[lo]) / duration + pred_xyz = _hermite(xyz[lo], xyz[hi], v0, v1, duration, offsets) + + r0, r1 = Rotation.from_quat(quat[lo]), Rotation.from_quat(quat[hi]) + end_rotvec = (r1 * r0.inv()).as_rotvec() + w0 = _median_angular_velocity(quat, left) + w1 = _median_angular_velocity(quat, right) + pred_rotvec = _hermite( + np.zeros(3), end_rotvec, w0, w1, duration, offsets) + pred_quat = (Rotation.from_rotvec(pred_rotvec) * r0).as_quat() + + forward_xyz = xyz[lo] + offsets[:, None] * v0 + backward_xyz = xyz[hi] - (hi - rows)[:, None] * v1 + pred_trans_disagreement = float(np.max( + np.linalg.norm(forward_xyz - backward_xyz, axis=1) * 1000.0)) + forward_q = (Rotation.from_rotvec(offsets[:, None] * w0) * r0).as_quat() + backward_q = ( + Rotation.from_rotvec(-(hi - rows)[:, None] * w1) * r1).as_quat() + pred_rot_disagreement = float(np.max( + _rotation_error_deg(forward_q, backward_q))) + pred = np.concatenate([pred_xyz, pred_quat], axis=1) + evidence = { + "left_anchor": lo, + "right_anchor": hi, + "left_context_frames": int(len(left)), + "right_context_frames": int(len(right)), + "prediction_translation_max_mm": pred_trans_disagreement, + "prediction_rotation_max_deg": pred_rot_disagreement, + } + return pred, evidence + + +def _endpoint_se3(pose: np.ndarray, bout: AnomalyBout) -> tuple[ + np.ndarray, dict[str, float]]: + """Interpolate a short bout from its immediate finite SE(3) anchors.""" + lo, hi = bout.start - 1, bout.end + 1 + rows = np.arange(bout.start, bout.end + 1) + duration = hi - lo + fraction = (rows - lo) / float(duration) + xyz = ((1.0 - fraction[:, None]) * pose[lo, :3] + + fraction[:, None] * pose[hi, :3]) + rotations = Slerp( + [0.0, 1.0], Rotation.from_quat(_unit_quaternion(pose[[lo, hi], 3:]))) + quat = rotations(fraction).as_quat() + anchor_translation_mm = float( + np.linalg.norm(pose[hi, :3] - pose[lo, :3]) * 1000.0) + anchor_rotation_deg = float(_quaternion_angle_deg( + pose[lo:lo + 1, 3:], pose[hi:hi + 1, 3:])[0]) + return np.concatenate([xyz, quat], axis=1), { + "left_anchor": int(lo), + "right_anchor": int(hi), + "anchor_translation_mm": anchor_translation_mm, + "anchor_rotation_deg": anchor_rotation_deg, + "prediction_translation_max_mm": anchor_translation_mm / duration, + "prediction_rotation_max_deg": anchor_rotation_deg / duration, + } + + +def _trajectory_is_physical(pose: np.ndarray, config: RepairConfig) -> tuple[bool, float, float]: + rot, trans = transition_metrics(pose) + finite = np.isfinite(rot) & np.isfinite(trans) + if not finite.all(): + return False, float("inf"), float("inf") + max_rot = float(np.max(rot, initial=0.0)) + max_trans = float(np.max(trans, initial=0.0)) + return (max_rot <= config.max_step_rotation_deg + and max_trans <= config.max_step_translation_mm), max_rot, max_trans + + +def _residuals(observed: np.ndarray, + predicted: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + finite = _finite_pose_rows(observed) + trans = np.full(len(observed), np.inf) + rot = np.full(len(observed), np.inf) + trans[finite] = np.linalg.norm( + observed[finite, :3] - predicted[finite, :3], axis=1) * 1000.0 + if finite.any(): + rot[finite] = _rotation_error_deg( + observed[finite, 3:], predicted[finite, 3:]) + return trans, rot, finite + + +def _refit_from_inliers(predicted: np.ndarray, observed: np.ndarray, + inlier: np.ndarray, offsets: np.ndarray, + duration: int) -> np.ndarray: + """Fit a boundary-zero cubic correction without moving either anchor.""" + if int(inlier.sum()) < 2: + return predicted + s = offsets / float(duration) + basis = np.stack([s * (1.0 - s), + s * (1.0 - s) * (2.0 * s - 1.0)], axis=1) + design = basis[inlier] + xyz_residual = observed[inlier, :3] - predicted[inlier, :3] + xyz_coef, *_ = np.linalg.lstsq(design, xyz_residual, rcond=None) + corrected_xyz = predicted[:, :3] + basis @ xyz_coef + + measured_r = Rotation.from_quat(observed[inlier, 3:]) + predicted_r = Rotation.from_quat(predicted[inlier, 3:]) + rot_residual = (measured_r * predicted_r.inv()).as_rotvec() + rot_coef, *_ = np.linalg.lstsq(design, rot_residual, rcond=None) + correction = Rotation.from_rotvec(basis @ rot_coef) + corrected_q = (correction * Rotation.from_quat(predicted[:, 3:])).as_quat() + return np.concatenate([corrected_xyz, corrected_q], axis=1) + + +def _robust_upper(values: np.ndarray, floor: float, cap: float) -> float: + values = np.asarray(values, dtype=float) + values = values[np.isfinite(values)] + if not len(values): + return float(floor) + centre = float(np.median(values)) + mad = float(np.median(np.abs(values - centre))) + return float(min(cap, max(floor, centre + 6.0 * 1.4826 * mad))) + + +def _local_inlier_thresholds(pose: np.ndarray, left: np.ndarray, + right: np.ndarray, + config: RepairConfig) -> tuple[float, float]: + """Estimate clean local jitter, bounded by global physical residual caps.""" + trans_accel: list[np.ndarray] = [] + rot_accel: list[np.ndarray] = [] + for rows in (left, right): + runs = np.split(rows, np.where(np.diff(rows) != 1)[0] + 1) + for run in runs: + if len(run) < 3: + continue + xyz = pose[run, :3] + trans_accel.append( + np.linalg.norm(np.diff(xyz, n=2, axis=0), axis=1) * 1000.0) + rotations = Rotation.from_quat(_unit_quaternion(pose[run, 3:])) + velocity = (rotations[1:] * rotations[:-1].inv()).as_rotvec() + rot_accel.append(np.degrees( + np.linalg.norm(np.diff(velocity, axis=0), axis=1))) + trans_values = np.concatenate(trans_accel) if trans_accel else np.empty(0) + rot_values = np.concatenate(rot_accel) if rot_accel else np.empty(0) + return ( + _robust_upper(trans_values, 3.0, config.translation_inlier_mm), + _robust_upper(rot_values, 2.0, config.rotation_inlier_deg), + ) + + +def _robust_branch_fit(observed: np.ndarray, initial: np.ndarray, + forced: np.ndarray, offsets: np.ndarray, duration: int, + translation_threshold_mm: float, + rotation_threshold_deg: float) -> tuple[ + np.ndarray, np.ndarray, np.ndarray, np.ndarray, + int, bool]: + """Alternate branch classification and trajectory correction up to 5 times.""" + predicted = initial.copy() + previous: np.ndarray | None = None + converged = False + iterations = 0 + for iterations in range(1, 6): + trans, rot, finite = _residuals(observed, predicted) + inlier = (finite + & (trans <= translation_threshold_mm) + & (rot <= rotation_threshold_deg) + & ~forced) + if previous is not None and np.array_equal(inlier, previous): + converged = True + break + previous = inlier.copy() + predicted = _refit_from_inliers( + predicted, observed, inlier, offsets, duration) + trans, rot, finite = _residuals(observed, predicted) + inlier = (finite + & (trans <= translation_threshold_mm) + & (rot <= rotation_threshold_deg) + & ~forced) + if previous is not None and np.array_equal(inlier, previous): + converged = True + return predicted, inlier, trans, rot, iterations, converged + + +def repair_pose_stream( + pose: np.ndarray, + side: str, + known_gaps: Iterable[tuple[int, int]] = (), + *, + task_gate: TaskGate | None = None, + config: RepairConfig | None = None, +) -> PoseRepairResult: + """Return candidate poses and explicit side-specific repair provenance. + + HIGH candidates are valid for training. MEDIUM candidates contain the + proposed reconstruction but remain invalid pending a review decision. + LOW events preserve the measured poses and remain invalid. + """ + raw = np.asarray(pose, dtype=float) + if raw.ndim != 2 or raw.shape[1] != 7: + raise ValueError(f"pose must have shape (T, 7), got {raw.shape}") + cfg = config or RepairConfig() + gate = task_gate or TaskGate() + gap_list = [(int(start), int(count)) for start, count in known_gaps] + bouts = detect_bouts(raw, side, gap_list, config=cfg) + finite = _finite_pose_rows(raw) + candidate = raw.copy() + repaired = np.zeros(len(raw), dtype=bool) + confidence = np.full(len(raw), Confidence.NONE, dtype=np.uint8) + valid = finite.copy() + event_id = np.full(len(raw), "", dtype=object) + events: list[RepairEvent] = [] + known_rows = { + row for start, count in gap_list + for row in range(max(0, start), min(len(raw), start + count)) + } + + for bout in bouts: + rows = np.arange(bout.start, bout.end + 1) + left, right = _context_rows(finite, bout.start, bout.end, + cfg.context_frames) + eid = f"{side}:{bout.start}-{bout.end}:{bout.kind}" + event_id[rows] = eid + if not len(left) or not len(right): + confidence[rows] = Confidence.LOW + valid[rows] = False + events.append(RepairEvent( + eid, bout, Confidence.LOW, "unrepaired_missing_anchor", (), { + "duration_frames": int(len(rows)), + "left_context_frames": int(len(left)), + "right_context_frames": int(len(right)), + })) + continue + + endpoint_method = ( + 1 <= len(rows) <= 5 + and int(left[-1]) == bout.start - 1 + and int(right[0]) == bout.end + 1 + ) + if endpoint_method: + predicted, evidence = _endpoint_se3(raw, bout) + evidence.update({ + "left_context_frames": int(len(left)), + "right_context_frames": int(len(right)), + }) + else: + predicted, evidence = _predict_bout(raw, bout, left, right) + observed = raw[rows] + forced = np.array([int(row) in known_rows for row in rows]) + lo = int(left[-1]); hi = int(right[0]) + trans_threshold, rot_threshold = _local_inlier_thresholds( + raw, left, right, cfg) + if endpoint_method: + # Keep this identical to the benchmarked linear/SLERP model; + # fitting a polynomial to possible wrong-branch observations + # would quietly turn it into a different estimator. + trans_residual, rot_residual, observed_finite = _residuals(observed, predicted) + inlier = (observed_finite & ~forced + & (trans_residual <= trans_threshold) + & (rot_residual <= rot_threshold)) + iterations, converged = 0, True + else: + predicted, inlier, trans_residual, rot_residual, iterations, converged = ( + _robust_branch_fit(observed, predicted, forced, rows - lo, + hi - lo, trans_threshold, rot_threshold)) + replace = ~inlier | forced + classified_outlier = replace & ~forced + if classified_outlier.any(): + outlier_separation = np.maximum( + trans_residual[classified_outlier] / trans_threshold, + rot_residual[classified_outlier] / rot_threshold) + branch_unambiguous = bool(np.min(outlier_separation) >= 1.5) + else: + branch_unambiguous = True + proposed = raw.copy() + proposed[rows[replace]] = predicted[replace] + physical, max_rot, max_trans = _trajectory_is_physical( + proposed[max(0, bout.start - 1):min(len(raw), bout.end + 2)], cfg) + evidence.update({ + "duration_frames": int(len(rows)), + "observed_inlier_frames": int(inlier.sum()), + "replaced_frames": int(replace.sum()), + "translation_residual_p95_mm": float(np.percentile(trans_residual, 95)), + "rotation_residual_p95_deg": float(np.percentile(rot_residual, 95)), + "reconstructed_max_step_translation_mm": max_trans, + "reconstructed_max_step_rotation_deg": max_rot, + "physical": bool(physical), + "fit_iterations": int(iterations), + "fit_converged": bool(converged), + "translation_inlier_threshold_mm": trans_threshold, + "rotation_inlier_threshold_deg": rot_threshold, + "branch_unambiguous": branch_unambiguous, + }) + extreme_prediction_disagreement = (not endpoint_method and ( + evidence["prediction_translation_max_mm"] + > 3.0 * cfg.max_prediction_translation_mm + or evidence["prediction_rotation_max_deg"] + > 3.0 * cfg.max_prediction_rotation_deg)) + if not physical or extreme_prediction_disagreement: + confidence[rows] = Confidence.LOW + valid[rows] = False + evidence["rejection"] = ( + "nonphysical_reconstruction" if not physical + else "incompatible_two_sided_prediction") + events.append(RepairEvent( + eid, bout, Confidence.LOW, "unrepaired_ambiguous_branch", (), + evidence)) + continue + two_full_contexts = (len(left) >= cfg.context_frames + and len(right) >= cfg.context_frames) + # A short boundary seed may actually be one end of a persistent + # alternate branch. Physical step limits alone cannot establish + # that the two anchors belong to the same trajectory branch. + endpoint_return = (not endpoint_method or + evidence["anchor_rotation_deg"] <= 20.0) + if endpoint_method: + evidence["endpoint_return_consistent"] = endpoint_return + prediction_agrees = endpoint_method or ( + evidence["prediction_translation_max_mm"] + <= cfg.max_prediction_translation_mm + and evidence["prediction_rotation_max_deg"] + <= cfg.max_prediction_rotation_deg) + high = ( + gate.high_confidence_enabled + and len(rows) <= min(cfg.max_high_frames, gate.validated_max_frames) + and (not endpoint_method or len(rows) <= gate.endpoint_max_frames) + and endpoint_return + and two_full_contexts and prediction_agrees and physical + and branch_unambiguous and bool(replace.any())) + tier = Confidence.HIGH if high else Confidence.MEDIUM + candidate[rows[replace]] = predicted[replace] + repaired[rows[replace]] = True + confidence[rows] = tier + valid[rows] = high + events.append(RepairEvent( + eid, bout, tier, ("endpoint_se3" if endpoint_method + else "robust_se3_hermite"), + tuple(rows[replace].astype(int)), evidence)) + + return PoseRepairResult( + pose=candidate, + repaired=repaired, + confidence=confidence, + valid=valid, + event_id=event_id, + events=tuple(events), + ) diff --git a/action_repair_tools/twm/react_preprocess/repaired_actions.py b/action_repair_tools/twm/react_preprocess/repaired_actions.py new file mode 100644 index 0000000000000000000000000000000000000000..4387630c6f9ccbfb2bd8860863556ba18e737b0e --- /dev/null +++ b/action_repair_tools/twm/react_preprocess/repaired_actions.py @@ -0,0 +1,125 @@ +"""Repair-aware action construction from candidate OptiTrack poses. + +Actions are 9-D ``[dx, dy, dz, relative_rotation_6d]``. Translation is in +metres. Rotation uses the first two columns of the world-frame relative +rotation matrix, concatenated column by column. +""" +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np +from scipy.spatial.transform import Rotation + + +MAX_ROTATION_DEG = 30.0 +MAX_TRANSLATION_MM = 50.0 + + +@dataclass(frozen=True) +class ActionSeries: + values: np.ndarray + valid: np.ndarray + repaired: np.ndarray + event_ids: tuple[tuple[str, ...], ...] + start_rows: np.ndarray + end_rows: np.ndarray + rotation_deg: np.ndarray + translation_mm: np.ndarray + + +def _check_pose_arrays(pose: np.ndarray, pose_valid: np.ndarray, + pose_repaired: np.ndarray, + pose_event_id: np.ndarray) -> tuple[ + np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + pose = np.asarray(pose, dtype=float) + if pose.ndim != 2 or pose.shape[1] != 7: + raise ValueError(f"pose must have shape (T, 7), got {pose.shape}") + valid = np.asarray(pose_valid, dtype=bool) + repaired = np.asarray(pose_repaired, dtype=bool) + event_id = np.asarray(pose_event_id, dtype=object) + for name, value in (("pose_valid", valid), ("pose_repaired", repaired), + ("pose_event_id", event_id)): + if value.shape != (len(pose),): + raise ValueError(f"{name} must have shape ({len(pose)},), got {value.shape}") + return pose, valid, repaired, event_id + + +def _event_union(*values: object) -> tuple[str, ...]: + out: set[str] = set() + for value in values: + if isinstance(value, str): + if value: + out.add(value) + elif value is not None: + out.update(str(item) for item in value if str(item)) + return tuple(sorted(out)) + + +def _relative_values(pose: np.ndarray, start: np.ndarray, + end: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + n = len(start) + values = np.zeros((n, 9), dtype=np.float32) + values[:, 3:] = np.array([1, 0, 0, 0, 1, 0], dtype=np.float32) + rotation_deg = np.full(n, np.inf, dtype=float) + translation_mm = np.full(n, np.inf, dtype=float) + endpoints_finite = np.isfinite(pose[start]).all(axis=1) & np.isfinite(pose[end]).all(axis=1) + q0_norm = np.linalg.norm(pose[start, 3:], axis=1) + q1_norm = np.linalg.norm(pose[end, 3:], axis=1) + endpoints_finite &= (q0_norm > 1e-12) & (q1_norm > 1e-12) + usable = np.flatnonzero(endpoints_finite) + if len(usable): + s, e = start[usable], end[usable] + delta = pose[e, :3] - pose[s, :3] + q0 = pose[s, 3:] / np.linalg.norm(pose[s, 3:], axis=1, keepdims=True) + q1 = pose[e, 3:] / np.linalg.norm(pose[e, 3:], axis=1, keepdims=True) + relative = Rotation.from_quat(q1) * Rotation.from_quat(q0).inv() + matrix = relative.as_matrix() + rot6d = np.concatenate([matrix[:, :, 0], matrix[:, :, 1]], axis=1) + values[usable, :3] = delta.astype(np.float32) + values[usable, 3:] = rot6d.astype(np.float32) + rotation_deg[usable] = np.degrees(relative.magnitude()) + translation_mm[usable] = np.linalg.norm(delta, axis=1) * 1000.0 + return values, rotation_deg, translation_mm, endpoints_finite + + +def native_actions(pose: np.ndarray, pose_valid: np.ndarray, + pose_repaired: np.ndarray, + pose_event_id: np.ndarray) -> ActionSeries: + """Build each native transition ``k -> k+1`` with explicit validity.""" + pose, pose_valid, pose_repaired, pose_event_id = _check_pose_arrays( + pose, pose_valid, pose_repaired, pose_event_id) + start = np.arange(max(0, len(pose) - 1), dtype=np.int32) + end = start + 1 + values, rotation_deg, translation_mm, finite = _relative_values( + pose, start, end) + valid = (pose_valid[start] & pose_valid[end] & finite + & (rotation_deg <= MAX_ROTATION_DEG) + & (translation_mm <= MAX_TRANSLATION_MM)) + repaired = pose_repaired[start] | pose_repaired[end] + event_ids = tuple(_event_union(pose_event_id[a], pose_event_id[b]) + for a, b in zip(start, end)) + return ActionSeries(values, valid, repaired, event_ids, start, end, + rotation_deg, translation_mm) + + +def actions_fps15(pose: np.ndarray, native: ActionSeries) -> ActionSeries: + """Build ``k -> k+2`` actions; validity is AND and repair is OR.""" + pose = np.asarray(pose, dtype=float) + expected_native = max(0, len(pose) - 1) + if len(native.valid) != expected_native: + raise ValueError( + f"native has {len(native.valid)} rows for {len(pose)} poses; " + f"expected {expected_native}") + start = np.arange(0, max(0, len(pose) - 2), 2, dtype=np.int32) + end = start + 2 + values, rotation_deg, translation_mm, _ = _relative_values(pose, start, end) + first = start + second = start + 1 + valid = native.valid[first] & native.valid[second] + repaired = native.repaired[first] | native.repaired[second] + event_ids = tuple(_event_union(native.event_ids[a], native.event_ids[b]) + for a, b in zip(first, second)) + return ActionSeries(values, valid, repaired, event_ids, start, end, + rotation_deg, translation_mm) + diff --git a/action_repair_tools/twm/react_toolbox/__init__.py b/action_repair_tools/twm/react_toolbox/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d1cae7aa09343b0199fb97074410c338ad95ca61 --- /dev/null +++ b/action_repair_tools/twm/react_toolbox/__init__.py @@ -0,0 +1 @@ +"""Coordinate conversion helpers used by action propagation.""" diff --git a/action_repair_tools/twm/react_toolbox/frames.py b/action_repair_tools/twm/react_toolbox/frames.py new file mode 100644 index 0000000000000000000000000000000000000000..cdfbdc6bb4a42dabdffd1b9c4ce12987763b3792 --- /dev/null +++ b/action_repair_tools/twm/react_toolbox/frames.py @@ -0,0 +1,150 @@ +"""Which way is up — declared, and converted as one piece or not at all. + +WHAT THE DATA IS, AND WHAT IT WAS + +The release is now **Z-up, right-handed**: the table normal in world +coordinates is (0.053, -0.056, 0.997), 4.4 degrees off +z, and both the pose +rotations and `T_mocap_to_cam` have determinant +1. + +OptiTrack RECORDS Y-up. The release used to ship that unchanged and said so +nowhere, which is the worse half of the problem: robotics code overwhelmingly +assumes Z-up, and a reader taking `pose[2]` as height got a horizontal +coordinate with nothing to complain — plausible numbers, fine-looking plots, +and an error that surfaces only as a model that never learns which way gravity +points. + +Poses and extrinsics were converted together, so every projection is +unchanged and every rendered preview, overlay and clip stayed valid. Only the +numbers moved. `ZUP_TO_YUP` converts back for anything that still wants the +raw OptiTrack convention. + +THE CONVERSION, AND THE TRAP IN IT + +Y-up to Z-up preserving handedness is a rotation of -90 degrees about x: + + (x, y, z) -> (x, -z, y) + +There are two right-handed candidates; this is the one that sends UP to **+z** +rather than -z. The other, R_x(+90), is equally right-handed and puts the table +normal at (0.053, 0.056, -0.997) — upside down, det still +1, and no assertion +about handedness would catch it. + +THE TRAP: a world-frame rotation must be applied to the POSES **and** to +`T_mocap_to_cam` together. Applied to one only, every projection moves and +nothing raises. So this module converts a BUNDLE — poses and cameras — and +`scripts/test_frames.py` asserts that projections are bit-identical afterwards. +Invariance is the test: if a pixel moves, the conversion was half-applied. + +The gel centre is in the sensor's own RIGID frame and is therefore untouched. +""" +from __future__ import annotations + +import numpy as np + +UP_AXIS_RECORDED = "z" +UP_AXIS_ROBOTICS = "z" + +# (x, y, z)_yup -> (x, -z, y)_zup. det = +1, and it sends +y to +z. +YUP_TO_ZUP = np.array([[1.0, 0.0, 0.0], + [0.0, 0.0, -1.0], + [0.0, 1.0, 0.0]]) +ZUP_TO_YUP = YUP_TO_ZUP.T + + +def _R(to_zup: bool) -> np.ndarray: + return YUP_TO_ZUP if to_zup else ZUP_TO_YUP + + +def convert_poses(poses7, to_zup: bool = True) -> np.ndarray: + """Rotate a pose array into the other up-convention. (N, 7) or (7,). + + Positions rotate; orientations PRE-multiply, because this is a change of + the world frame, not a motion of the body. + """ + from scipy.spatial.transform import Rotation + + p = np.atleast_2d(np.asarray(poses7, float)).copy() + M = _R(to_zup) + ok = np.isfinite(p).all(1) & (np.linalg.norm(p[:, 3:7], axis=1) > 0.5) + # A column can be entirely untracked -- pushT never tracked an object + # body, so its object_pose is NaN from end to end. scipy raises "Found + # zero norm quaternions" on the resulting EMPTY array, and that exception + # is why the pushT half of the release stayed unconverted while the + # motherboard half went Z-up. Nothing to rotate is not an error. + if ok.any(): + p[ok, :3] = p[ok, :3] @ M.T + p[ok, 3:7] = (Rotation.from_matrix(M) + * Rotation.from_quat(p[ok, 3:7])).as_quat() + return p[0] if np.ndim(poses7) == 1 else p + + +def convert_calibration(cal: dict, to_zup: bool = True) -> dict: + """The same rotation applied to every camera extrinsic. Returns a copy. + + `T_mocap_to_cam` maps mocap -> camera. Rotating the mocap frame by M means + the new matrix is `T @ M^-1`, so that `T_new @ (M @ x) == T @ x` and every + projection is unchanged. Getting this inverse backwards is the other way to + break it silently; the invariance test catches that too. + """ + M = _R(to_zup) + out = {k: v for k, v in cal.items()} + out["up_axis"] = "z" if to_zup else "y" + out["cams"] = {} + for name, c in cal["cams"].items(): + T = np.asarray(c["T_mocap_to_cam"], float).copy() + T[:3, :3] = T[:3, :3] @ M.T # M^-1 == M.T for a rotation + out["cams"][name] = {**c, "T_mocap_to_cam": T} + return out + + +def to_zup(poses7, cal: dict): + """Convert a pose array and its calibration together. Returns (poses, cal). + + Use this rather than the two halves: the whole point is that they move as + one piece. + """ + return convert_poses(poses7, True), convert_calibration(cal, True) + + +def require_up_axis(cal: dict, expected: str = UP_AXIS_RECORDED, where: str = ""): + """Raise unless `cal` declares the up-axis convention `expected`. + + WHY THIS EXISTS. Poses and calibration are two halves of one convention, + and they are read from two paths. Rotating only one half leaves every + self-consistency check green -- projections still recompute exactly from + the same wrong matrix -- while the pictures are wrong. That happened: the + probe test set drew its poses from the Z-up release and its calibration + from a Y-up tree, and every overlay was a median 153 px off. + + So the halves must be paired loudly, not by convention. A file with no + declaration is treated as the pre-conversion Y-up it was. + """ + got = cal.get("up_axis") + if got != expected: + raise ValueError( + f"calibration up-axis mismatch{' in ' + where if where else ''}: " + f"declared {got!r}, need {expected!r}. Poses and calibration must " + f"come from the same release. A missing declaration means a " + f"pre-conversion Y-up file -- convert it with " + f"scripts/convert_release_zup.py, or take the calibration from " + f"the release the poses came from.") + return cal + + +def as_up_axis(cal: dict, want: str) -> dict: + """Return `cal` in the convention `want`, converting only if it must. + + `require_up_axis` refuses a mismatch; this one repairs it. Use it when the + caller genuinely knows which convention its POSES are in -- a raw-HDF5 + reader wants "y", a release reader wants "z" -- and should not have to care + which directory the calibration happened to come from. + + An undeclared calibration is the pre-conversion Y-up it was, so this + converts it rather than trusting it. + """ + if want not in ("y", "z"): + raise ValueError(f"up axis must be 'y' or 'z', got {want!r}") + got = cal.get("up_axis") or "y" + if got == want: + return cal + return convert_calibration(cal, to_zup=(want == "z")) diff --git a/action_repair_tools/twm/scripts/__init__.py b/action_repair_tools/twm/scripts/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5c49aa0ed6ed3fa13616bedc95e7c20fc553963c --- /dev/null +++ b/action_repair_tools/twm/scripts/__init__.py @@ -0,0 +1 @@ +"""Action audit, reconstruction, publication and review commands.""" diff --git a/action_repair_tools/twm/scripts/benchmark_mocap_branch.py b/action_repair_tools/twm/scripts/benchmark_mocap_branch.py new file mode 100644 index 0000000000000000000000000000000000000000..94385608823b76bdecc01e858afed25053891ae7 --- /dev/null +++ b/action_repair_tools/twm/scripts/benchmark_mocap_branch.py @@ -0,0 +1,119 @@ +"""Benchmark body-frame branch correction on frozen clean pose fragments.""" +from __future__ import annotations + +import argparse +from dataclasses import asdict +from pathlib import Path + +import numpy as np +from scipy.spatial.transform import Rotation + +from twm.react_preprocess.mocap_branch import find_branch_candidates +from twm.react_preprocess.mocap_candidate import load_manifest +from twm.scripts.benchmark_mocap_repair import ( + _candidate_order, _native_errors, _rotation_angle_deg, _select_disjoint, _summary, +) +from twm.scripts.build_mocap_repair_candidates import _atomic_json, _clean_fragments + + +def _inject(pose, start, length): + observed = pose.copy() + rotations = Rotation.from_quat(pose[start:start+length, 3:]) + observed[start:start+length, :3] += rotations.apply([0.029, 0, 0]) + observed[start:start+length, 3:] = (rotations*Rotation.from_euler('yx', [100, 8], degrees=True)).as_quat() + return observed + + +def _negative_controls(): + pose = np.zeros((180, 7)); pose[:, 6] = 1 + fast = pose.copy() + fast[:, 3:] = Rotation.from_euler('z', 35*np.arange(len(fast)), degrees=True).as_quat() + out_back = pose.copy() + angle = np.r_[np.zeros(30), np.linspace(0, 100, 60), np.linspace(100, 0, 60), np.zeros(30)] + out_back[:, 3:] = Rotation.from_euler('z', angle, degrees=True).as_quat() + abrupt = find_branch_candidates(_inject(pose, 40, 60)) + repeated = find_branch_candidates(_inject(_inject(pose, 20, 30), 100, 30)) + return { + 'continuous_fast_turn_candidates': len(find_branch_candidates(fast)), + 'continuous_out_back_candidates': len(find_branch_candidates(out_back)), + 'abrupt_out_back_candidates': len(abrupt), + 'abrupt_out_back_automatic_admissions': sum(c.confidence == 'HIGH' for c in abrupt), + 'repeated_abrupt_out_back_candidates': len(repeated), + 'repeated_abrupt_out_back_repeat_supported_candidates': sum(c.evidence['matching_return_pairs'] >= 2 for c in repeated), + 'caveat': 'Repeated abrupt genuine motion is observationally indistinguishable from a tracker branch; repetition alone is not independent ground truth.', + } + + +def benchmark_branch_task(clean_episodes, *, task, max_intervals=200, seed=17): + episodes = [np.asarray(p, dtype=float) for p in clean_episodes] + lengths = (6, 20, 60, 120) + selected = _select_disjoint(_candidate_order( + episodes, lengths, np.random.default_rng(seed), pool_size=max(1000, max_intervals*50)), max_intervals) + names = ('translation_mm', 'orientation_deg', 'native_translation_mm', 'native_rotation_deg') + values = {name: [] for name in names} + all_values = {name: [] for name in names} + by_length = {length: {name: [] for name in names} for length in lengths} + counts = {length: 0 for length in lengths} + for interval in selected: + truth = episodes[interval.episode][interval.start-15:interval.start+interval.length+15] + observed = _inject(truth, 15, interval.length) + candidates = find_branch_candidates(observed) + matches = [c for c in candidates if c.start == 15 and c.end == 15+interval.length-1] + result = observed.copy() + if matches: + result[15:15+interval.length] = matches[0].pose + counts[interval.length] += 1 + rows = slice(15, 15+interval.length) + trans = np.linalg.norm(result[rows, :3]-truth[rows, :3], axis=1)*1000 + rot = _rotation_angle_deg(result[rows, 3:], truth[rows, 3:]) + action_trans, action_rot = _native_errors(result, truth, np.arange(14, 15+interval.length)) + for name, error in zip(names, (trans, rot, action_trans, action_rot)): + all_values[name].extend(error.tolist()) + if matches: + values[name].extend(error.tolist()) + by_length[interval.length][name].extend(error.tolist()) + metrics = {name: asdict(_summary(v)) for name, v in values.items()} + per_length = {str(length): {'qualifying_intervals': counts[length], + 'metrics': {name: asdict(_summary(v)) for name, v in by_length[length].items()}} + for length in lengths} + def passes(measured): + caps = {'translation_mm': (2, 10), 'orientation_deg': (1, 5), + 'native_translation_mm': (2, 5), 'native_rotation_deg': (1, 3)} + return all(measured[name]['count'] > 0 and measured[name]['median'] <= median and + measured[name]['p95'] <= p95 for name, (median, p95) in caps.items()) + negative = _negative_controls() + passed = (sum(counts.values()) >= 100 and passes(metrics) and + all(counts[length] >= 10 and passes(per_length[str(length)]['metrics']) for length in lengths) and + negative['continuous_fast_turn_candidates'] == 0 and + negative['continuous_out_back_candidates'] == 0) + return { + 'schema_version': 1, 'task': task, 'seed': seed, 'method': 'paired_body_branch', + 'intervals': len(selected), 'qualifying_intervals': sum(counts.values()), + 'rejected_intervals': len(selected)-sum(counts.values()), 'anomaly_lengths': list(lengths), + 'metrics': metrics, 'metrics_all': {name: asdict(_summary(v)) for name, v in all_values.items()}, + 'by_length': per_length, 'negative_controls': negative, + 'gate': {'high_confidence_enabled': False, 'reconstruction_accuracy_passed': bool(passed), + 'validated_max_frames': max(lengths) if passed else 0, + 'requires_independent_branch_identity_evidence': True}, + 'scope': 'Reconstruction accuracy conditional on exact paired-boundary identification; does not validate anomaly classification or branch identity.', + } + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--candidate-root', type=Path, required=True) + parser.add_argument('--output-root', type=Path, required=True) + parser.add_argument('--max-intervals', type=int, default=200) + parser.add_argument('--seed', type=int, default=17) + args = parser.parse_args() + manifest = load_manifest(args.candidate_root/'input_manifest.json') + for task in manifest.tasks: + fragments = _clean_fragments(manifest, task) + report = benchmark_branch_task(fragments, task=task, max_intervals=args.max_intervals, seed=args.seed) + report['manifest_digest'] = manifest.digest + path = _atomic_json(args.output_root/f'{task}.json', report) + print(task, report['qualifying_intervals'], report['gate'], path, flush=True) + + +if __name__ == '__main__': + main() diff --git a/action_repair_tools/twm/scripts/benchmark_mocap_repair.py b/action_repair_tools/twm/scripts/benchmark_mocap_repair.py new file mode 100644 index 0000000000000000000000000000000000000000..0f0e4a91b26c09131c074de5e4f24dff8537756d --- /dev/null +++ b/action_repair_tools/twm/scripts/benchmark_mocap_repair.py @@ -0,0 +1,315 @@ +#!/usr/bin/env python3 +"""Calibrate mocap repair confidence with deterministic clean-data masking.""" +from __future__ import annotations + +from dataclasses import asdict, dataclass, field +from typing import Iterable + +import numpy as np +from scipy.spatial.transform import Rotation + +from twm.react_preprocess.mocap_repair import TaskGate, repair_pose_stream + + +@dataclass(frozen=True) +class MetricSummary: + median: float + p95: float + count: int + + +@dataclass(frozen=True) +class BenchmarkReport: + task: str + seed: int + intervals: int + high_intervals: int + anomaly_lengths: tuple[int, ...] + confidence_counts: dict[str, int] + metrics: dict[str, MetricSummary] + metrics_all: dict[str, MetricSummary] + gate: TaskGate + methods: dict = field(default_factory=dict) + + def to_dict(self) -> dict: + return { + "schema_version": 1, + "task": self.task, + "seed": self.seed, + "intervals": self.intervals, + "high_intervals": self.high_intervals, + "anomaly_lengths": list(self.anomaly_lengths), + "confidence_counts": dict(sorted(self.confidence_counts.items())), + "metrics": {name: asdict(self.metrics[name]) + for name in sorted(self.metrics)}, + "metrics_all": {name: asdict(self.metrics_all[name]) + for name in sorted(self.metrics_all)}, + "gate": asdict(self.gate), + "methods": self.methods, + } + + +@dataclass(frozen=True) +class _Candidate: + episode: int + start: int + length: int + motion: float + + +def _validate_episode(pose: np.ndarray) -> np.ndarray: + pose = np.asarray(pose, dtype=float) + if pose.ndim != 2 or pose.shape[1] != 7: + raise ValueError(f"clean episode must have shape (T, 7), got {pose.shape}") + return pose + + +def _rotation_angle_deg(a: np.ndarray, b: np.ndarray) -> np.ndarray: + return np.degrees( + (Rotation.from_quat(a) * Rotation.from_quat(b).inv()).magnitude()) + + +def _motion_score(pose: np.ndarray, start: int, length: int) -> float: + lo, hi = start - 1, start + length + trans = np.linalg.norm(pose[hi, :3] - pose[lo, :3]) * 1000.0 + rot = float(_rotation_angle_deg( + pose[hi:hi + 1, 3:], pose[lo:lo + 1, 3:])[0]) + return float(trans + rot) + + +def _candidate_order(episodes: list[np.ndarray], lengths: tuple[int, ...], + rng: np.random.Generator, *, + pool_size: int) -> list[_Candidate]: + """Sample a bounded candidate pool before motion-stratified sorting.""" + context = 15 + spans: list[tuple[int, int, int]] = [] + for episode_index, pose in enumerate(episodes): + for length in lengths: + count = max(0, len(pose) - 2 * context - length) + if count: + spans.append((episode_index, length, count)) + total = sum(count for _, _, count in spans) + if not total: + return [] + take = min(int(pool_size), total) + flat_rows = (np.arange(total, dtype=np.int64) if take == total + else np.sort(rng.choice(total, size=take, replace=False))) + cumulative = np.cumsum([count for _, _, count in spans]) + candidates = [] + for flat in flat_rows: + span_index = int(np.searchsorted(cumulative, flat, side="right")) + prior = int(cumulative[span_index - 1]) if span_index else 0 + episode_index, length, _ = spans[span_index] + start = context + int(flat) - prior + pose = episodes[episode_index] + rows = pose[start - context:start + length + context] + norm = np.linalg.norm(rows[:, 3:], axis=1) + if not np.isfinite(rows).all() or np.any(norm <= 1e-12): + continue + candidates.append(_Candidate( + episode_index, start, length, + _motion_score(pose, start, length))) + candidates.sort(key=lambda row: (row.motion, row.episode, row.start, row.length)) + bins = [list(chunk) for chunk in np.array_split( + np.asarray(candidates, dtype=object), 3) if len(chunk)] + for rows in bins: + rng.shuffle(rows) + ordered: list[_Candidate] = [] + while any(bins): + for rows in bins: + if rows: + ordered.append(rows.pop()) + return ordered + + +def _select_disjoint(candidates: list[_Candidate], count: int) -> list[_Candidate]: + used: dict[int, list[tuple[int, int]]] = {} + out = [] + for candidate in candidates: + end = candidate.start + candidate.length + overlaps = any(candidate.start < prior_end and prior_start < end + for prior_start, prior_end in used.get(candidate.episode, [])) + if overlaps: + continue + used.setdefault(candidate.episode, []).append((candidate.start, end)) + out.append(candidate) + if len(out) >= count: + break + return out + + +def _inject_wrong_branch(pose: np.ndarray, rows: np.ndarray) -> np.ndarray: + observed = pose.copy() + observed[rows, :3] += np.array([0.029, -0.003, 0.0]) + wrong = Rotation.from_euler("yx", [100.0, 8.0], degrees=True) + observed[rows, 3:] = ( + wrong * Rotation.from_quat(pose[rows, 3:])).as_quat() + return observed + + +def _native_errors(candidate: np.ndarray, truth: np.ndarray, + transitions: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + truth_delta = truth[transitions + 1, :3] - truth[transitions, :3] + candidate_delta = candidate[transitions + 1, :3] - candidate[transitions, :3] + trans = np.linalg.norm(candidate_delta - truth_delta, axis=1) * 1000.0 + truth_relative = (Rotation.from_quat(truth[transitions + 1, 3:]) + * Rotation.from_quat(truth[transitions, 3:]).inv()) + candidate_relative = (Rotation.from_quat(candidate[transitions + 1, 3:]) + * Rotation.from_quat(candidate[transitions, 3:]).inv()) + rot = np.degrees((candidate_relative * truth_relative.inv()).magnitude()) + return trans, rot + + +def _summary(values: list[float]) -> MetricSummary: + data = np.asarray(values, dtype=float) + if not len(data): + return MetricSummary(float("inf"), float("inf"), 0) + return MetricSummary( + median=float(np.median(data)), + p95=float(np.percentile(data, 95)), + count=int(len(data)), + ) + + +def benchmark_task( + clean_episodes: Iterable[np.ndarray], + *, + task: str, + anomaly_lengths: Iterable[int] = (1, 5, 20, 60), + max_intervals: int = 300, + seed: int = 0, +) -> BenchmarkReport: + """Mask held-out clean intervals and score reconstruction/action errors.""" + episodes = [_validate_episode(pose) for pose in clean_episodes] + lengths = tuple(sorted({int(length) for length in anomaly_lengths + if int(length) > 0})) + if not episodes: + raise ValueError("at least one clean episode is required") + if not lengths: + raise ValueError("at least one positive anomaly length is required") + if max_intervals <= 0: + raise ValueError("max_intervals must be positive") + rng = np.random.default_rng(int(seed)) + selected = _select_disjoint( + _candidate_order( + episodes, lengths, rng, + pool_size=max(1000, int(max_intervals) * 50)), + int(max_intervals)) + + names = ("translation_mm", "orientation_deg", + "native_translation_mm", "native_rotation_deg") + all_values: dict[str, list[float]] = {name: [] for name in names} + high_values: dict[str, list[float]] = {name: [] for name in names} + high_by_length: dict[int, dict[str, list[float]]] = { + length: {name: [] for name in names} for length in lengths} + high_interval_count = {length: 0 for length in lengths} + method_values = {} + method_counts = {} + confidence_counts = {"HIGH": 0, "MEDIUM": 0, "LOW": 0} + endpoint_lengths = [length for length in lengths if length <= 5] + endpoint_trial_max = max(endpoint_lengths, default=0) + gate_for_reconstruction = TaskGate( + True, max(lengths), endpoint_max_frames=endpoint_trial_max) + for index, interval in enumerate(selected): + # Only the 15-frame contexts influence reconstruction. Evaluating + # an entire many-minute clean episode for every masked window adds + # quadratic detection work without supplying any extra evidence. + context_start = interval.start - 15 + context_end = interval.start + interval.length + 15 + truth = episodes[interval.episode][context_start:context_end] + interval = _Candidate(interval.episode, 15, interval.length, interval.motion) + all_rows = np.arange(interval.start, interval.start + interval.length) + if index % 2: + corrupt_rows = all_rows[::2] + known_gaps: list[tuple[int, int]] = [] + else: + corrupt_rows = all_rows + known_gaps = [(interval.start, interval.length)] + observed = _inject_wrong_branch(truth, corrupt_rows) + result = repair_pose_stream( + observed, "left", known_gaps, + task_gate=gate_for_reconstruction) + pose_error = np.linalg.norm( + result.pose[all_rows, :3] - truth[all_rows, :3], axis=1) * 1000.0 + rot_error = _rotation_angle_deg( + result.pose[all_rows, 3:], truth[all_rows, 3:]) + lo = max(0, interval.start - 1) + hi = min(len(truth) - 1, interval.start + interval.length) + transitions = np.arange(lo, hi, dtype=int) + trans_action, rot_action = _native_errors( + result.pose, truth, transitions) + interval_values = { + "translation_mm": list(map(float, pose_error)), + "orientation_deg": list(map(float, rot_error)), + "native_translation_mm": list(map(float, trans_action)), + "native_rotation_deg": list(map(float, rot_action)), + } + for name in names: + all_values[name].extend(interval_values[name]) + corrupt_confidence = result.confidence[corrupt_rows] + if (len(corrupt_rows) + and result.repaired[corrupt_rows].all() + and np.all(corrupt_confidence == 3)): + tier = "HIGH" + elif (result.repaired[corrupt_rows].any() + or np.any(corrupt_confidence == 2)): + tier = "MEDIUM" + else: + tier = "LOW" + confidence_counts[tier] += 1 + if tier == "HIGH": + methods = {event.method for event in result.events + if event.bout.start <= interval.start + interval.length - 1 + and event.bout.end >= interval.start} + method = next(iter(methods)) if len(methods) == 1 else "mixed" + method_counts[method] = method_counts.get(method, 0) + 1 + values = method_values.setdefault(method, {name: [] for name in names}) + for name in names: + values[name].extend(interval_values[name]) + high_interval_count[interval.length] += 1 + for name in names: + high_values[name].extend(interval_values[name]) + high_by_length[interval.length][name].extend( + interval_values[name]) + + metrics = {name: _summary(high_values[name]) for name in names} + metrics_all = {name: _summary(all_values[name]) for name in names} + + def passes(values: dict[str, list[float]], interval_count: int) -> bool: + summary = {name: _summary(values[name]) for name in names} + return ( + interval_count >= 100 + and summary["translation_mm"].median <= 2.0 + and summary["translation_mm"].p95 <= 10.0 + and summary["orientation_deg"].median <= 1.0 + and summary["orientation_deg"].p95 <= 5.0 + and summary["native_translation_mm"].p95 <= 5.0 + and summary["native_rotation_deg"].p95 <= 3.0) + + validated_max = 0 + for max_length in lengths: + prefix = {name: [] for name in names} + prefix_intervals = 0 + for length in lengths: + if length > max_length: + continue + prefix_intervals += high_interval_count[length] + for name in names: + prefix[name].extend(high_by_length[length][name]) + if passes(prefix, prefix_intervals): + validated_max = max_length + gate = TaskGate( + high_confidence_enabled=validated_max > 0, + validated_max_frames=validated_max, + endpoint_max_frames=(endpoint_trial_max if passes( + method_values.get("endpoint_se3", {name: [] for name in names}), + method_counts.get("endpoint_se3", 0)) else 0)) + return BenchmarkReport( + task=str(task), seed=int(seed), intervals=len(selected), + high_intervals=confidence_counts["HIGH"], + anomaly_lengths=lengths, confidence_counts=confidence_counts, + metrics=metrics, metrics_all=metrics_all, gate=gate, + methods={method: {"intervals": method_counts[method], + "metrics": {name: asdict(_summary(values[name])) + for name in names}} + for method, values in method_values.items()}) diff --git a/action_repair_tools/twm/scripts/build_mocap_repair_candidates.py b/action_repair_tools/twm/scripts/build_mocap_repair_candidates.py new file mode 100644 index 0000000000000000000000000000000000000000..12fe387f1297880bde037d626ac76d839fa64c13 --- /dev/null +++ b/action_repair_tools/twm/scripts/build_mocap_repair_candidates.py @@ -0,0 +1,350 @@ +#!/usr/bin/env python3 +"""Audit, calibrate, build, review, and verify mocap repair candidates.""" +from __future__ import annotations + +import argparse +from collections import Counter +from dataclasses import asdict +import json +import os +from pathlib import Path +import tempfile +from typing import Any, Iterable + +import numpy as np +import pyarrow.parquet as pq + +from twm.react_preprocess.mocap_candidate import ( + CandidateBuild, + CandidateWriter, + InputManifest, + VerificationReport, + apply_decisions, + load_manifest, + save_manifest, + snapshot_inputs, + verify_candidate, +) +from twm.react_preprocess.mocap_repair import ( + TaskGate, + detect_bouts, +) +from twm.scripts.benchmark_mocap_repair import ( + BenchmarkReport, + benchmark_task, +) + + +TASKS = ("motherboard", "pushT", "rope", "toy") +DEFAULT_SOURCE = Path("/media/yxma/Disk1/twm/release") +DEFAULT_OUTPUT = Path( + "/media/yxma/Disk1/twm/review/mocap_repair_2026-09-17/candidate_release") + + +def _atomic_json(path: Path, value: Any) -> Path: + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile( + mode="w", encoding="utf-8", dir=path.parent, + prefix=f".{path.name}.", suffix=".tmp", delete=False) as stream: + temp = Path(stream.name) + json.dump(value, stream, indent=2, sort_keys=True) + stream.write("\n") + os.replace(temp, path) + return path + + +def _gap_map(source_root: Path, task: str) -> dict[str, Any]: + path = source_root / task / "pose_gaps.json" + return json.loads(path.read_text()) if path.is_file() else {} + + +def _known_gaps(gaps: dict[str, Any], date: str, episode: str, + side: str) -> list[tuple[int, int]]: + return [tuple(map(int, row)) for row in + gaps.get(f"{date}/{episode}", {}).get(side, [])] + + +def _load_pinned_manifest(candidate_root: Path) -> InputManifest: + path = Path(candidate_root) / "input_manifest.json" + if not path.is_file(): + raise ValueError(f"run audit first; missing {path}") + return load_manifest(path) + + +def run_audit(source_root: Path = DEFAULT_SOURCE, + candidate_root: Path = DEFAULT_OUTPUT, + tasks: Iterable[str] = TASKS) -> InputManifest: + """Freeze input membership and report metadata-only anomaly bouts.""" + source_root = Path(source_root).resolve() + candidate_root = Path(candidate_root).resolve() + manifest = snapshot_inputs(source_root, tasks) + # Constructor performs the production-root overlap safety check before the + # audit writes even its first JSON file. + CandidateWriter(source_root, candidate_root, manifest) + save_manifest(manifest, candidate_root / "input_manifest.json") + + counts: dict[str, Counter] = {task: Counter() for task in manifest.tasks} + frames: dict[str, int] = {task: 0 for task in manifest.tasks} + gap_cache = {task: _gap_map(source_root, task) for task in manifest.tasks} + for item in manifest.files: + task, _, date, filename = Path(item.path).parts + episode = Path(filename).stem + table = pq.read_table(source_root / item.path) + for side in ("left", "right"): + name = f"sensor_{side}_pose" + if name not in table.column_names: + continue + pose = np.asarray(table[name].to_pylist(), float) + bouts = detect_bouts( + pose, side, + _known_gaps(gap_cache[task], date, episode, side)) + for bout in bouts: + counts[task][bout.kind] += 1 + frames[task] += bout.end - bout.start + 1 + _atomic_json(candidate_root / "audit_summary.json", { + "schema_version": 1, + "manifest_digest": manifest.digest, + "source_root": str(source_root), + "episode_count": len(manifest.files), + "tasks": {task: { + "episodes": sum(Path(item.path).parts[0] == task + for item in manifest.files), + "bouts": dict(sorted(counts[task].items())), + "candidate_frames": frames[task], + } for task in manifest.tasks}, + }) + return manifest + + +def _clean_fragments(manifest: InputManifest, task: str, + context: int = 15) -> list[np.ndarray]: + source_root = Path(manifest.source_root) + gaps = _gap_map(source_root, task) + fragments: list[np.ndarray] = [] + for item in manifest.files: + if Path(item.path).parts[0] != task: + continue + _, _, date, filename = Path(item.path).parts + episode = Path(filename).stem + table = pq.read_table(source_root / item.path) + for side in ("left", "right"): + name = f"sensor_{side}_pose" + if name not in table.column_names: + continue + pose = np.asarray(table[name].to_pylist(), float) + blocked = ~np.isfinite(pose).all(axis=1) + for bout in detect_bouts( + pose, side, _known_gaps(gaps, date, episode, side)): + lo = max(0, bout.start - context) + hi = min(len(pose), bout.end + context + 1) + blocked[lo:hi] = True + starts = np.flatnonzero(~blocked & np.r_[True, blocked[:-1]]) + ends = np.flatnonzero(~blocked & np.r_[blocked[1:], True]) + 1 + for start, end in zip(starts, ends): + if end - start >= 40: + fragments.append(pose[start:end].copy()) + return fragments + + +def run_benchmarks(candidate_root: Path = DEFAULT_OUTPUT, *, + anomaly_lengths: Iterable[int] = (1, 5, 20, 60), + max_intervals: int = 300, + seed: int = 0) -> dict[str, BenchmarkReport]: + """Calibrate one immutable, explicit confidence gate per task.""" + candidate_root = Path(candidate_root).resolve() + manifest = _load_pinned_manifest(candidate_root) + # Preflight source digests before treating any source pose as clean truth. + verifier = CandidateWriter( + Path(manifest.source_root), candidate_root, manifest) + for item in manifest.files: + verifier._verify_source(item) + reports: dict[str, BenchmarkReport] = {} + benchmark_root = candidate_root / "benchmarks" + for task in manifest.tasks: + fragments = _clean_fragments(manifest, task) + if not fragments: + raise ValueError(f"no clean pose fragments available for {task}") + report = benchmark_task( + fragments, task=task, anomaly_lengths=anomaly_lengths, + max_intervals=max_intervals, seed=seed) + reports[task] = report + _atomic_json(benchmark_root / f"{task}.json", report.to_dict()) + _atomic_json(candidate_root / "task_gates.json", { + "schema_version": 1, + "manifest_digest": manifest.digest, + "tasks": {task: asdict(report.gate) + for task, report in sorted(reports.items())}, + }) + return reports + + +def _load_task_gates(candidate_root: Path, + manifest: InputManifest) -> dict[str, TaskGate]: + path = Path(candidate_root) / "task_gates.json" + if not path.is_file(): + raise ValueError(f"run benchmark first; missing {path}") + payload = json.loads(path.read_text()) + if payload.get("manifest_digest") != manifest.digest: + raise ValueError("task gates manifest digest mismatch") + missing = set(manifest.tasks) - set(payload.get("tasks", {})) + if missing: + raise ValueError(f"task gates missing tasks: {sorted(missing)}") + return {task: TaskGate(**payload["tasks"][task]) + for task in manifest.tasks} + + +def run_build(candidate_root: Path = DEFAULT_OUTPUT, *, + task_gates: dict[str, TaskGate] | None = None, + branch_review: bool = False, + require_loss_evidence: bool = False) -> CandidateBuild: + candidate_root = Path(candidate_root).resolve() + manifest = _load_pinned_manifest(candidate_root) + gates = task_gates or _load_task_gates(candidate_root, manifest) + missing = set(manifest.tasks) - set(gates) + if missing: + raise ValueError(f"task gates missing tasks: {sorted(missing)}") + writer = CandidateWriter( + Path(manifest.source_root), candidate_root, manifest, + task_gates=gates, branch_review=branch_review, + require_loss_evidence=require_loss_evidence) + build = writer.write_all() + _atomic_json(candidate_root / "candidate_summary.json", + summarize_candidate(candidate_root)) + return build + + +def run_verify(candidate_root: Path = DEFAULT_OUTPUT) -> VerificationReport: + report = verify_candidate(candidate_root) + _atomic_json(Path(candidate_root) / "verification.json", { + "schema_version": 1, + "episodes": report.episodes, + "errors": list(report.errors), + }) + return report + + +def summarize_candidate(candidate_root: Path = DEFAULT_OUTPUT) -> dict[str, Any]: + candidate_root = Path(candidate_root).resolve() + manifest = _load_pinned_manifest(candidate_root) + summary: dict[str, Any] = { + "schema_version": 1, + "manifest_digest": manifest.digest, + "tasks": {}, + } + for task in manifest.tasks: + row = { + "events": {"HIGH": 0, "MEDIUM": 0, "LOW": 0}, + "repaired_frames": 0, + "native_repaired_actions": 0, + "native_recovered_valid_actions": 0, + "fps15_repaired_actions": 0, + "fps15_recovered_valid_actions": 0, + "unresolved_events": 0, + } + for path in sorted((candidate_root / task / "repair_events").glob("*/*.json")): + for event in json.loads(path.read_text()).get("events", []): + confidence = str(event["confidence"]).upper() + row["events"].setdefault(confidence, 0) + row["events"][confidence] += 1 + row["repaired_frames"] += len(event.get("replaced_frames", [])) + if confidence in {"MEDIUM", "LOW"} and event.get("decision") != "accept_repair": + row["unresolved_events"] += 1 + for rate, repaired_key, recovered_key in ( + ("actions_native", "native_repaired_actions", + "native_recovered_valid_actions"), + ("actions_fps15", "fps15_repaired_actions", + "fps15_recovered_valid_actions")): + for path in sorted((candidate_root / task / rate).glob("*/*.npz")): + with np.load(path, allow_pickle=False) as data: + repaired = np.asarray(data["repaired"], bool) + valid = np.asarray(data["valid"], bool) + row[repaired_key] += int(repaired.sum()) + row[recovered_key] += int((repaired & valid).sum()) + summary["tasks"][task] = row + return summary + + +def run_review(candidate_root: Path = DEFAULT_OUTPUT, *, render: bool = False, + review_root: Path | None = None, + high_sample_rate: float = 0.1, + sample_seed: int = 0) -> dict[str, Any]: + from twm.scripts.build_pose_review import build_review + + candidate_root = Path(candidate_root).resolve() + manifest = _load_pinned_manifest(candidate_root) + source_root = Path(manifest.source_root) + review_root = (Path(review_root).resolve() if review_root is not None + else candidate_root.parent / "review_package") + results = {} + for task in manifest.tasks: + results[task] = build_review( + source_root / task, review_root / task, render=render, + candidate_task_root=candidate_root / task, + high_sample_rate=high_sample_rate, + sample_seed=sample_seed) + return results + + +def _parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output", type=Path, default=DEFAULT_OUTPUT) + sub = parser.add_subparsers(dest="command", required=True) + audit = sub.add_parser("audit") + audit.add_argument("--source", type=Path, default=DEFAULT_SOURCE) + audit.add_argument("--task", action="append", choices=TASKS) + bench = sub.add_parser("benchmark") + bench.add_argument("--max-intervals", type=int, default=300) + bench.add_argument("--seed", type=int, default=0) + build = sub.add_parser("build") + build.add_argument("--branch-review", action="store_true") + build.add_argument("--require-loss-evidence", action="store_true") + review = sub.add_parser("review") + review.add_argument("--render", action="store_true") + review.add_argument("--review-root", type=Path) + review.add_argument("--high-sample-rate", type=float, default=0.1) + review.add_argument("--seed", type=int, default=0) + decisions = sub.add_parser("apply-decisions") + decisions.add_argument("decisions", type=Path) + sub.add_parser("verify") + sub.add_parser("summary") + return parser + + +def main(argv: list[str] | None = None) -> int: + args = _parser().parse_args(argv) + if args.command == "audit": + result = run_audit(args.source, args.output, args.task or TASKS) + print(json.dumps(result.to_dict(), indent=2, sort_keys=True)) + elif args.command == "benchmark": + result = run_benchmarks( + args.output, max_intervals=args.max_intervals, seed=args.seed) + print(json.dumps({task: report.to_dict() + for task, report in result.items()}, indent=2, + sort_keys=True)) + elif args.command == "build": + result = run_build(args.output, branch_review=args.branch_review, + require_loss_evidence=args.require_loss_evidence) + print(f"episodes={len(result.episodes)} manifest={result.manifest_digest}") + elif args.command == "review": + result = run_review( + args.output, render=args.render, review_root=args.review_root, + high_sample_rate=args.high_sample_rate, sample_seed=args.seed) + print(json.dumps({task: len(value["review_events"]) + for task, value in result.items()}, indent=2, + sort_keys=True)) + elif args.command == "apply-decisions": + apply_decisions(args.output, json.loads(args.decisions.read_text())) + print(json.dumps(summarize_candidate(args.output), indent=2, + sort_keys=True)) + elif args.command == "verify": + report = run_verify(args.output) + print(f"episodes={report.episodes} errors={len(report.errors)}") + elif args.command == "summary": + print(json.dumps(summarize_candidate(args.output), indent=2, + sort_keys=True)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/action_repair_tools/twm/scripts/export_old_motherboard_actions.py b/action_repair_tools/twm/scripts/export_old_motherboard_actions.py new file mode 100644 index 0000000000000000000000000000000000000000..0ecaaef8ddf81d3a21de7bb18d946e8eec7e32b4 --- /dev/null +++ b/action_repair_tools/twm/scripts/export_old_motherboard_actions.py @@ -0,0 +1,240 @@ +"""Export old motherboard parquet with repaired T-row LeRobot actions. + +Raw release columns are preserved. Candidate poses and action validity come +from the provenance-safe mocap repair tree; unresolved events stay invalid. +""" +from __future__ import annotations + +import argparse +from datetime import datetime, timezone +import hashlib +import json +import os +from pathlib import Path +import tempfile +from typing import Mapping, Sequence + +import numpy as np +import pyarrow as pa +import pyarrow.parquet as pq + + +TASK = "motherboard" +DATES = ("2026-05-10", "2026-05-11", "2026-05-19") +EXPECTED_EPISODES = 32 +SIDES = ("left", "right") + + +def _fixed_float32(values: np.ndarray, width: int) -> pa.Array: + flat = pa.array(np.asarray(values, np.float32).reshape(-1), type=pa.float32()) + return pa.FixedSizeListArray.from_arrays(flat, width) + + +def _append(table: pa.Table, name: str, values: pa.Array) -> pa.Table: + if name in table.column_names: + raise ValueError(f"refusing to replace existing column {name!r}") + return table.append_column(name, values) + + +def _long_transition_mask(events: Sequence[Mapping[str, object]], side: str, + n_rows: int) -> np.ndarray: + result = np.zeros(n_rows, dtype=bool) + for event in events: + if str(event.get("side")) != side: + continue + start, end = int(event["start"]), int(event["end"]) + if end - start + 1 <= 60: + continue + first = max(0, start - 1) + last = min(n_rows - 2, end) + if last >= first: + result[first:last + 1] = True + return result + + +def build_action_episode(raw: pa.Table, candidate: pa.Table, + native_left: Mapping[str, np.ndarray], + native_right: Mapping[str, np.ndarray], + events: Sequence[Mapping[str, object]]) -> pa.Table: + """Return raw columns plus repaired pose/action columns, aligned to T rows.""" + if raw.num_rows != candidate.num_rows: + raise ValueError("raw and candidate row counts differ") + n = raw.num_rows + if n < 1: + raise ValueError("empty episode") + repaired_pose = {} + native = {"left": native_left, "right": native_right} + for side in SIDES: + repaired_pose[side] = np.asarray( + candidate[f"sensor_{side}_pose"].to_pylist(), dtype=np.float32) + if repaired_pose[side].shape != (n, 7): + raise ValueError(f"candidate {side} pose must have shape ({n}, 7)") + for key in ("valid", "repaired"): + if np.asarray(native[side][key]).shape != (n - 1,): + raise ValueError(f"native {side} {key} must have {n - 1} rows") + + target = np.concatenate([ + np.concatenate([repaired_pose["left"][1:], + repaired_pose["right"][1:]], axis=1), + np.concatenate([repaired_pose["left"][-1:], + repaired_pose["right"][-1:]], axis=1), + ], axis=0) + out = raw + for side in SIDES: + out = _append(out, f"sensor_{side}_pose_repaired", + _fixed_float32(repaired_pose[side], 7)) + out = _append(out, "action", _fixed_float32(target, 14)) + + for side in SIDES: + valid = np.r_[np.asarray(native[side]["valid"], bool), False] + repaired = np.r_[np.asarray(native[side]["repaired"], bool), False] + pose_conf = np.asarray( + candidate[f"pose_{side}_repair_confidence"].to_numpy(), + dtype=np.uint8) + confidence = np.zeros(n, dtype=np.uint8) + if n > 1: + confidence[:-1] = np.maximum(pose_conf[:-1], pose_conf[1:]) + event_overlap = np.zeros(n, dtype=bool) + for event in events: + if str(event.get("side")) != side: + continue + start, end = int(event["start"]), int(event["end"]) + first, last = max(0, start - 1), min(n - 2, end) + if last >= first: + event_overlap[first:last + 1] = True + lost = (~valid) & event_overlap + long_lost = _long_transition_mask(events, side, n) & lost + out = _append(out, f"action_valid_{side}", pa.array(valid)) + out = _append(out, f"action_repaired_{side}", pa.array(repaired)) + out = _append(out, f"action_repair_confidence_{side}", + pa.array(confidence, type=pa.uint8())) + out = _append(out, f"action_lost_track_{side}", pa.array(lost)) + out = _append(out, f"action_long_lost_track_{side}", + pa.array(long_lost)) + return out + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def _load_native(path: Path) -> dict[str, np.ndarray]: + with np.load(path, allow_pickle=False) as data: + return {name: np.asarray(data[name]) for name in data.files} + + +def _atomic_parquet(table: pa.Table, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile(dir=path.parent, prefix=f".{path.name}.", + suffix=".tmp", delete=False) as stream: + temp = Path(stream.name) + try: + pq.write_table(table, temp, compression="zstd") + os.replace(temp, path) + finally: + temp.unlink(missing_ok=True) + + +def _columns_equal(left: pa.ChunkedArray, right: pa.ChunkedArray) -> bool: + if left.type != right.type or len(left) != len(right): + return False + if left.equals(right): + return True + try: + if (pa.types.is_list(left.type) or pa.types.is_large_list(left.type) + or pa.types.is_fixed_size_list(left.type)): + a = np.asarray(left.to_pylist(), dtype=float) + b = np.asarray(right.to_pylist(), dtype=float) + elif pa.types.is_floating(left.type): + a = np.asarray(left.to_numpy(zero_copy_only=False), dtype=float) + b = np.asarray(right.to_numpy(zero_copy_only=False), dtype=float) + else: + return False + except (TypeError, ValueError): + return False + return bool(np.array_equal(a, b, equal_nan=True)) + + +def export_tree(release: Path, candidate_root: Path, output: Path) -> dict: + release, candidate_root, output = map(Path, (release, candidate_root, output)) + sources = [p for date in DATES + for p in sorted((release / TASK / "meta" / date).glob("episode_*.parquet"))] + if len(sources) != EXPECTED_EPISODES: + raise ValueError(f"expected {EXPECTED_EPISODES} episodes, found {len(sources)}") + episodes = [] + totals = {"valid_left": 0, "valid_right": 0, + "repaired_left": 0, "repaired_right": 0, + "lost_left": 0, "lost_right": 0, + "long_left": 0, "long_right": 0} + for source in sources: + date, name = source.parent.name, source.name + candidate = candidate_root / TASK / "meta" / date / name + event_path = candidate_root / TASK / "repair_events" / date / f"{source.stem}.json" + if not candidate.is_file() or not event_path.is_file(): + raise FileNotFoundError(f"missing candidate artifacts for {date}/{source.stem}") + raw_table, candidate_table = pq.read_table(source), pq.read_table(candidate) + event_doc = json.loads(event_path.read_text()) + events = event_doc.get("events", []) + native = { + side: _load_native(candidate_root / TASK / "actions_native" / date / + f"{source.stem}_{side}.npz") + for side in SIDES + } + table = build_action_episode(raw_table, candidate_table, + native["left"], native["right"], events) + for field in raw_table.schema: + if field.name not in table.column_names or table.schema.field(field.name) != field: + raise ValueError(f"original schema changed: {date}/{name}:{field.name}") + if not _columns_equal(raw_table[field.name], table[field.name]): + raise ValueError(f"original values changed: {date}/{name}:{field.name}") + dest = output / "meta" / date / name + _atomic_parquet(table, dest) + event_dest = output / "action_repair_events" / date / f"{source.stem}.json" + event_dest.parent.mkdir(parents=True, exist_ok=True) + event_dest.write_bytes(event_path.read_bytes()) + stats = {} + for side in SIDES: + for key, col in (("valid", f"action_valid_{side}"), + ("repaired", f"action_repaired_{side}"), + ("lost", f"action_lost_track_{side}"), + ("long", f"action_long_lost_track_{side}")): + value = int(np.asarray(table[col].to_numpy()).sum()) + stats[f"{key}_{side}"] = value + totals[f"{key}_{side}"] += value + episodes.append({"date": date, "episode": source.stem, + "rows": table.num_rows, "source_sha256": _sha256(source), + "parquet_sha256": _sha256(dest), **stats}) + manifest = { + "schema_version": 1, + "created_utc": datetime.now(timezone.utc).isoformat(), + "task": TASK, + "dates": list(DATES), + "episodes": episodes, + "totals": totals, + "action": "next-frame repaired left pose (7) + right pose (7)", + "terminal_action_valid": False, + "long_lost_track_min_frames": 61, + } + (output / "action_update_manifest.json").write_text( + json.dumps(manifest, indent=2, sort_keys=True) + "\n") + return manifest + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--release", type=Path, required=True) + parser.add_argument("--candidate", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + manifest = export_tree(args.release, args.candidate, args.output) + print(f"episodes={len(manifest['episodes'])} errors=0") + print(json.dumps(manifest["totals"], sort_keys=True)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/action_repair_tools/twm/scripts/propagate_mocap_repairs.py b/action_repair_tools/twm/scripts/propagate_mocap_repairs.py new file mode 100644 index 0000000000000000000000000000000000000000..6a9cacfffe189035ffc8467116002578fb806d6e --- /dev/null +++ b/action_repair_tools/twm/scripts/propagate_mocap_repairs.py @@ -0,0 +1,522 @@ +"""Map repaired source poses to every published segment without altering raw data. + +Inventory pins remote membership/digests and records a verified coordinate +conversion. Export keeps every non-action base column, including force columns. +""" +from __future__ import annotations + +import argparse +from collections import Counter +from html import escape +import json +from pathlib import Path +import re +from urllib.parse import quote + +import numpy as np +import pyarrow as pa +import pyarrow.parquet as pq + +from twm.react_toolbox.frames import convert_poses +from twm.react_preprocess.mocap_candidate import _write_actions +from twm.react_preprocess.repaired_actions import native_actions, actions_fps15 +from twm.scripts.export_old_motherboard_actions import ( + build_action_episode, _atomic_parquet, _columns_equal, _sha256) + + +SIDES = ('left', 'right') +POSES = tuple(f'sensor_{s}_pose' for s in SIDES) + + +def source_relative_path(remote_path: str | Path) -> Path: + parts = Path(remote_path).parts + if len(parts) != 5 or parts[0] not in ('data', 'old_data') or parts[2] != 'meta': + raise ValueError(f'unsupported remote path: {remote_path}') + return Path(*parts[1:-1], re.sub(r'_seg\d+(?=\.parquet$)', '', parts[-1])) + + +def _values(table, column): + array = table[column].combine_chunks() + if (pa.types.is_list(array.type) or pa.types.is_fixed_size_list(array.type)) and not array.null_count: + if len(array) and len(array.values) % len(array) == 0: + return array.values.to_numpy(zero_copy_only=False).reshape(len(array), -1) + return np.asarray(array.to_pylist()) + + +def _coordinate_mode(raw: pa.Table, selected: pa.Table) -> str: + if all(np.array_equal(_values(raw, c), _values(selected, c), equal_nan=True) + for c in POSES): + return 'identity' + if all(np.array_equal(_values(raw, c), convert_poses(_values(selected, c)), + equal_nan=True) for c in POSES): + return 'yup_to_zup' + raise ValueError('sensor pose mismatch: neither identity nor verified Y-up to Z-up') + + +def align_rows(raw: pa.Table, source: pa.Table) -> np.ndarray: + """Require unique source IDs, consecutive rows, exact times and known poses.""" + if raw.num_rows == 0: + raise ValueError('empty segment') + ids = source['source_h5_frame'].to_pylist() + if len(set(ids)) != len(ids): + raise ValueError('duplicate source_h5_frame in source') + index = {value: i for i, value in enumerate(ids)} + try: + rows = np.asarray([index[value] for value in raw['source_h5_frame'].to_pylist()], + dtype=np.int64) + except KeyError as exc: + raise ValueError(f'missing source_h5_frame: {exc}') from exc + if np.any(np.diff(rows) != 1): + raise ValueError('segment source rows must be contiguous and unique') + selected = source.take(pa.array(rows)) + if not np.array_equal(_values(raw, 'timestamp'), _values(selected, 'timestamp')): + raise ValueError('timestamp mismatch') + _coordinate_mode(raw, selected) + return rows + + +def _derived(name): + return (name == 'action' or name.startswith('action_') + or name in ('sensor_left_pose_repaired', 'sensor_right_pose_repaired') + or name.startswith(('pose_left_', 'pose_right_'))) + + +def build_segment(raw: pa.Table, source: pa.Table, candidate: pa.Table, + source_rows: np.ndarray, events: list[dict]): + """Return augmented parquet and native/fps15 ActionSeries for each side. + + Recompute transitions from candidate pose validity after selecting the + published segment, so neither actions nor 15fps phase cross a cut boundary. + """ + rows = align_rows(raw, source) + if not np.array_equal(rows, source_rows): + raise ValueError('provided source rows disagree with verified mapping') + if candidate.num_rows != source.num_rows: + raise ValueError('candidate/source row counts differ') + for c in ('source_h5_frame', 'timestamp'): + if not _columns_equal(source[c], candidate[c]): + raise ValueError(f'candidate/source {c} mismatch') + segment = candidate.take(pa.array(rows)) + mode = _coordinate_mode(raw, source.take(pa.array(rows))) + sidecars = {} + for side in SIDES: + required = [f'pose_{side}_{key}' for key in + ('valid', 'repaired', 'repair_confidence', 'repair_event_id')] + for name in required: + if name not in segment.column_names: + raise ValueError(f'missing candidate provenance: {name}') + column = f'sensor_{side}_pose' + pose = _values(segment, column) + if mode == 'yup_to_zup': + pose = convert_poses(pose) + segment = segment.set_column(segment.schema.get_field_index(column), + column, pa.array(pose.tolist())) + native = native_actions(pose, _values(segment, required[0]), + _values(segment, required[1]), + _values(segment, required[3])) + sidecars[side] = {'native': native, 'fps15': actions_fps15(pose, native)} + # Keep original event duration; negative local starts are intentional and + # retain long-loss classification for events clipped by the segment edge. + local_events = [dict(event, start=int(event['start']) - int(rows[0]), + end=int(event['end']) - int(rows[0])) + for event in events + if int(event['end']) >= rows[0] - 1 + and int(event['start']) <= rows[-1] + 1] + base = raw.select([name for name in raw.column_names if not _derived(name)]) + out = build_action_episode(base, segment, + {k: getattr(sidecars['left']['native'], k) for k in ('valid', 'repaired')}, + {k: getattr(sidecars['right']['native'], k) for k in ('valid', 'repaired')}, + local_events) + for side in SIDES: + for key in ('valid', 'repaired', 'repair_confidence', 'repair_event_id'): + name = f'pose_{side}_{key}' + out = out.append_column(name, segment[name]) + method_name = f'pose_{side}_repair_method' + if method_name in segment.column_names: + out = out.append_column(method_name, segment[method_name]) + else: + methods = {event['event_id']: event.get('method') for event in events + if event.get('side') == side and 'event_id' in event} + values = [methods.get(event_id) for event_id in + segment[f'pose_{side}_repair_event_id'].to_pylist()] + out = out.append_column(method_name, pa.array(values, type=pa.string())) + for name in base.column_names: + if out.schema.field(name) != base.schema.field(name) or not _columns_equal(out[name], base[name]): + raise ValueError(f'preserved column changed: {name}') + return out, sidecars + + +def segment_reviews(remote_path: str, raw: pa.Table, output: pa.Table, + rows: np.ndarray, events: list[dict], video_files: set[str], + revision: str, include_high_charts: bool = False) -> list[dict]: + """Clip review coordinates to the published video, preserving source evidence.""" + remote = Path(remote_path) + capture_times = _values(raw, 'timestamp').astype(float) + capture_times -= capture_times[0] + # Published videos contain one frame per parquet row at constant 30 fps. + # Capture timestamps include acquisition stalls and are not media time. + times = np.arange(raw.num_rows, dtype=float) / 30. + records = [] + for event in events: + first, last = int(event['start']), int(event['end']) + if last < rows[0] or first > rows[-1]: + continue + lo, hi = max(0, first - int(rows[0])), min(len(rows) - 1, last - int(rows[0])) + side = event['side'] + event_id = event.get('event_id', f'{side}:{first}-{last}') + # Video filenames are included only when present in the frozen HF listing. + links = {} + for view in ('view_middle', f'wrist_{side}', 'view_left', 'view_right'): + video = str(Path(*remote.parts[:2], 'videos', remote.parts[3], + remote.stem, view + '.mp4')) + if video in video_files: + links[view] = (f'https://huggingface.co/datasets/yxma/React/resolve/' + f'{quote(revision or "main", safe="")}/{quote(video, safe="/")}' + f'#t={max(0., times[lo] - 1):.3f},{times[hi] + 1:.3f}') + record = dict(event, qualified_event_id=f'{remote_path}::{event_id}', + remote_path=remote_path, source_start_row=first, + source_end_row=last, local_start_row=lo, local_end_row=hi, + local_start_seconds=float(times[lo]), local_end_seconds=float(times[hi]), + capture_start_seconds=float(capture_times[lo]), + capture_end_seconds=float(capture_times[hi]), + video_time_basis='local_row / 30 fps', + video_links=links) + if include_high_charts or str(event.get('confidence', '')).upper() != 'HIGH': + a, b = max(0, lo - 30), min(len(rows), hi + 31) + sample = np.unique(np.r_[np.linspace(a, b - 1, min(160, b - a), dtype=int), lo, hi]) + chart = {'seconds': times[sample].tolist(), 'sampled': b - a > 160} + for label, column in [('raw', f'sensor_{side}_pose'), + ('repaired', f'sensor_{side}_pose_repaired')]: + context_start = max(0, a - 1) + pose = _values((output if label == 'repaired' else raw).slice( + context_start, b - context_start), column).astype(float) + # Angular displacement between adjacent native frames; no unwrap + # or interpolation is invented for missing/zero quaternions. + q = pose[:, 3:] + norm = np.linalg.norm(q, axis=1) + usable = np.isfinite(q).all(axis=1) & (norm > 1e-12) + unit = np.full(q.shape, np.nan) + unit[usable] = q[usable] / norm[usable, None] + angle = np.r_[np.nan, np.degrees(2 * np.arccos(np.clip( + np.abs(np.sum(unit[1:] * unit[:-1], axis=1)), 0, 1)))] + data = np.column_stack((pose[:, :3] * 1000., angle))[sample - context_start] + chart[label] = [[float(v) if np.isfinite(v) else None for v in row] for row in data] + record['chart'] = chart + records.append(record) + return records + + +def _json_safe(value): + """Represent unavailable nonfinite evidence as JSON null, never as a number.""" + if isinstance(value, float) and not np.isfinite(value): + return None + if isinstance(value, dict): + return {key: _json_safe(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [_json_safe(item) for item in value] + return value + + +def _review_svg(chart: dict) -> str: + times = np.asarray(chart['seconds']) + left, width, height = 70., 740., 64. + x = left + width * (times - times[0]) / max(1e-9, times[-1] - times[0]) + parts = [''] + for k, title in enumerate(('x (mm)', 'y (mm)', 'z (mm)', 'step (deg)')): + arrays = [np.asarray(chart[label], dtype=float)[:, k] for label in ('raw', 'repaired')] + finite = np.concatenate([a[np.isfinite(a)] for a in arrays]) + top = 10. + k * 78. + parts.append(f'{title}') + if not len(finite): + parts.append(f'Missing pose') + continue + low, high = float(finite.min()), float(finite.max()) + span = max(1e-6, high - low) + for values, color in zip(arrays, ('#bd3342', '#147cbb')): + y = top + height - (values - low) / span * height + # Separate path pieces at missing values instead of bridging gaps. + points, active = [], False + for px, py in zip(x, y): + if np.isfinite(py): + points.append(f'{"L" if active else "M"}{px:.1f},{py:.1f}') + active = True + else: + active = False + parts.append(f'') + parts.append(f'{high:.2f}') + parts.append(f'{low:.2f}') + parts.append(f'{times[0]:.2f}s{times[-1]:.2f}s') + return ''.join(parts) + + +def choose_review_samples(records: list[dict], count: int = 25) -> list[dict]: + """Prefer 20 algorithmic proposals + 5 other events, balancing all scopes. + + One source event appears at most once even if it overlaps several segments. + Selection uses the final candidate event records, never candidate labels from + an earlier build. A shortfall is reported by the exported selection manifest. + """ + unique = {} + for record in records: + source = source_relative_path(record['remote_path']) + scope = Path(record['remote_path']).parts[0] + key = (scope, str(source), record.get('event_id', record['qualified_event_id'])) + prior = unique.get(key) + if prior is None or record['local_end_row'] - record['local_start_row'] > prior['local_end_row'] - prior['local_start_row']: + unique[key] = record + def features(record): + length = record['source_end_row'] - record['source_start_row'] + 1 + duration = 'short_1_4' if length <= 4 else 'flicker_5_20' if length <= 20 else 'medium_21_60' if length <= 60 else 'long_over_60' + return ('/'.join(Path(record['remote_path']).parts[:2]), duration, record.get('kind', 'unknown')) + selected, used = [], Counter() + def take(pool, n): + pool = list(pool) + for _ in range(min(n, len(pool))): + def score(record): + group, duration, kind = features(record) + ambiguity = 'ambiguous' in str(record.get('method', '')) or 'incompatible' in str(record.get('evidence', {})) + return (100 / (1 + used[('scope', group)]) + + 20 / (1 + used[('duration', duration)]) + + 10 / (1 + used[('kind', kind)]) + + (15 if ambiguity and not used[('ambiguous', True)] else 0)) + choice = max(sorted(pool, key=lambda r: r['qualified_event_id']), key=score) + pool.remove(choice) + selected.append(choice) + group, duration, kind = features(choice) + for name, value in [('scope', group), ('duration', duration), ('kind', kind)]: + used[(name, value)] += 1 + if 'ambiguous' in str(choice.get('method', '')) or 'incompatible' in str(choice.get('evidence', {})): + used[('ambiguous', True)] += 1 + def proposed(record): + evidence = record.get('evidence', {}) + return (str(record.get('confidence', '')).upper() == 'HIGH' + or str(evidence.get('prior_confidence', record.get('prior_confidence', ''))).upper() == 'HIGH' + or evidence.get('independent_loss_evidence') is False) + high = [r for r in unique.values() if proposed(r)] + other = [r for r in unique.values() if not proposed(r)] + take(high, max(0, count - 5)) + take(other, min(5, count)) + selected_ids = {r['qualified_event_id'] for r in selected} + take([r for r in unique.values() if r['qualified_event_id'] not in selected_ids], count - len(selected)) + return [dict(record, human_verification_status='UNVERIFIED', + sample_group='algorithmic_proposal' if proposed(record) else 'other_uncertain') + for record in selected] + + +def review_html(records: list[dict], *, include_high: bool = False, + title: str = 'Mocap event review') -> str: + review = [r for r in records if include_high or str(r.get('confidence', '')).upper() != 'HIGH'] + parts = ['', + 'Mocap event review', + f'

{escape(title)}

{len(review)} {"selected" if include_high else "non-HIGH"} event occurrences across published segments. ', + 'Raw traces are red; repaired traces are blue. Missing values are gaps. ', + 'Each chart includes one second of context; long intervals are sampled. ', + 'Video links and charts use segment-local frame / 30 fps; capture-time offsets are listed separately. No approval is implied. ', + 'The measured camera footage is unchanged; before/after repair is shown in the red/blue pose charts. ', + 'These examples support human verification and are not proof that every jump is tracking loss. ', + 'Check visible hand/object motion against the measured jump, the surrounding frames, and the repair.

'] + for record in review: + eid = escape(record['qualified_event_id']) + confidence = escape(str(record.get('confidence', 'UNKNOWN'))) + method = escape(str(record.get('method', 'unavailable'))) + parts.append(f'
{eid} — {confidence} — {method} — ' + f'{record["local_start_seconds"]:.3f}–{record["local_end_seconds"]:.3f}s' + f'{" — UNVERIFIED" if include_high else ""}') + parts.append('

' + ''.join(f'{escape(view)}' + for view, url in record['video_links'].items()) + '

') + if not record['video_links']: + parts.append('

No matching video in the frozen repository inventory.

') + if include_high: + if record['video_links']: + parts.append('

' + ' ' + '

') + for view, url in record['video_links'].items(): + if view == 'view_middle' or view.startswith('wrist_'): + parts.append(f'

{escape(view)}

') + if 'chart' in record: + parts.append(_review_svg(record['chart'])) + parts.append('
' + escape(json.dumps({key: record.get(key) for key in
+            ('source_start_row', 'source_end_row', 'local_start_row', 'local_end_row', 'capture_start_seconds', 'capture_end_seconds', 'video_time_basis', 'kind', 'evidence')}, indent=2)) + '
') + if include_high: + parts.append('''''') + return ''.join(parts) + '' + + +def inventory(base_root: Path, source_root: Path, validation_source: Path, + revision: str = '') -> dict: + entries, errors = [], [] + cache = {} + for path in sorted(base_root.glob('*/*/meta/*/*.parquet')): + relative = path.relative_to(base_root) + source_relative = source_relative_path(relative) + root = validation_source if relative.parts[1] == 'validation' else source_root + source_path = root / source_relative + entry = {'remote_path': str(relative), 'base_sha256': _sha256(path), + 'source_path': str(source_path), 'candidate_relative': str(source_relative)} + try: + raw = pq.read_table(path) + if source_path not in cache: + cache[source_path] = pq.read_table(source_path) + source = cache[source_path] + rows = align_rows(raw, source) + entry.update(rows=len(rows), start_row=int(rows[0]), end_row=int(rows[-1]), + coordinate_mode=_coordinate_mode(raw, source.take(pa.array(rows))), + source_sha256=_sha256(source_path)) + entries.append(entry) + except (ValueError, FileNotFoundError, KeyError) as exc: + errors.append(dict(entry, error=str(exc))) + return {'schema_version': 1, 'revision': revision, 'base_root': str(base_root), + 'counts': dict(Counter('/'.join(Path(e['remote_path']).parts[:2]) for e in entries)), + 'episodes': entries, 'errors': errors} + + +def export_inventory(manifest: dict, candidate_root: Path, validation_candidate: Path, + output_root: Path, base_root: Path | None = None, + video_inventory: dict | None = None) -> dict: + if manifest['errors']: + raise ValueError('inventory has unresolved mapping errors') + base_root = Path(base_root or manifest['base_root']) + if output_root.resolve() == base_root.resolve(): + raise ValueError('output must differ from frozen base') + records, cache, review_records = [], {}, [] + video_inventory = video_inventory or {} + video_files = set(video_inventory.get('files', [])) + for entry in manifest['episodes']: + relative = Path(entry['remote_path']) + base_path = base_root / relative + # A refreshed base is allowed only after the caller freezes a new + # inventory. This prevents silently overwriting concurrently changed force. + if _sha256(base_path) != entry['base_sha256']: + raise ValueError(f'base digest changed: {relative}; regenerate inventory') + source_path = Path(entry['source_path']) + if _sha256(source_path) != entry['source_sha256']: + raise ValueError(f'source digest changed: {source_path}') + root = validation_candidate if relative.parts[1] == 'validation' else candidate_root + candidate_path = root / entry['candidate_relative'] + if candidate_path not in cache: + task, _, date, filename = Path(entry['candidate_relative']).parts + event_path = root / task / 'repair_events' / date / (Path(filename).stem + '.json') + cache[candidate_path] = (pq.read_table(source_path), pq.read_table(candidate_path), + json.loads(event_path.read_text())['events']) + source, candidate, events = cache[candidate_path] + raw = pq.read_table(base_path) + rows = np.arange(entry['start_row'], entry['end_row'] + 1) + out, sidecars = build_segment(raw, source, candidate, rows, events) + dest = output_root / relative + _atomic_parquet(out, dest) + for side, rates in sidecars.items(): + for rate, series in rates.items(): + side_path = output_root / relative.parts[0] / relative.parts[1] / ('actions_' + rate) / relative.parts[3] / (relative.stem + '_' + side + '.npz') + _write_actions(side_path, series) + reviews = segment_reviews(str(relative), raw, out, rows, events, video_files, + video_inventory.get('revision', manifest.get('revision', 'main'))) + review_records.extend(reviews) + review_path = output_root / relative.parts[0] / relative.parts[1] / 'action_repair_events' / relative.parts[3] / (relative.stem + '.json') + review_path.parent.mkdir(parents=True, exist_ok=True) + review_path.write_text(json.dumps(_json_safe({'schema_version': 1, 'remote_path': str(relative), + 'source_path': str(source_path), 'events': reviews}), indent=2, allow_nan=False) + '\n') + records.append(dict(entry, output_sha256=_sha256(dest), + valid_left=int(sum(out['action_valid_left'].to_pylist())), + valid_right=int(sum(out['action_valid_right'].to_pylist())))) + result = dict(manifest, episodes=records, output_root=str(output_root)) + result['review_event_occurrences'] = len(review_records) + result['non_high_review_occurrences'] = sum(str(r.get('confidence', '')).upper() != 'HIGH' for r in review_records) + (output_root / 'action_review.html').write_text(review_html(review_records)) + samples = choose_review_samples(review_records) + output_cache = {} + for i, sample in enumerate(samples): + if 'chart' in sample: + continue + relative = sample['remote_path'] + if relative not in output_cache: + output_cache[relative] = pq.read_table(output_root / relative) + table = output_cache[relative] + entry = next(e for e in manifest['episodes'] if e['remote_path'] == relative) + rows = np.arange(entry['start_row'], entry['end_row'] + 1) + samples[i] = segment_reviews(relative, table, table, rows, [sample], video_files, + video_inventory.get('revision', manifest.get('revision', 'main')), + include_high_charts=True)[0] + sample_manifest = {'requested_count': 25, 'actual_count': len(samples), + 'high_count': sum(str(s.get('confidence', '')).upper() == 'HIGH' for s in samples), + 'sample_group_counts': dict(Counter(s.get('sample_group', 'unknown') for s in samples)), + 'scope_counts': dict(Counter('/'.join(Path(s['remote_path']).parts[:2]) for s in samples)), + 'selection': 'Prefer 20 algorithmic proposals (HIGH/prior HIGH or explicitly lacking independent loss evidence) and 5 other uncertain; balance scopes, durations, kinds; deduplicate source events. All require human verification.', + 'limitation': 'Human verification examples, not proof that every jump is tracking loss.', + 'events': samples} + result['sample_review_count'] = len(samples) + (output_root / 'action_sample25.json').write_text(json.dumps(_json_safe(sample_manifest), indent=2, allow_nan=False) + '\n') + (output_root / 'action_sample25.html').write_text(review_html(samples, include_high=True, + title=f'{len(samples)} examples for human verification')) + (output_root / 'action_publication_manifest.json').write_text(json.dumps(result, indent=2) + '\n') + return result + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('command', choices=('inventory', 'export')) + parser.add_argument('--base', type=Path, required=True) + parser.add_argument('--source', type=Path, required=True) + parser.add_argument('--validation-source', type=Path, required=True) + parser.add_argument('--inventory', type=Path, required=True) + parser.add_argument('--revision', default='') + parser.add_argument('--candidate', type=Path) + parser.add_argument('--validation-candidate', type=Path) + parser.add_argument('--output', type=Path) + parser.add_argument('--remote-videos', type=Path, help='Frozen JSON with revision and MP4 files list') + args = parser.parse_args() + if args.command == 'inventory': + result = inventory(args.base, args.source, args.validation_source, args.revision) + args.inventory.parent.mkdir(parents=True, exist_ok=True) + args.inventory.write_text(json.dumps(result, indent=2) + '\n') + else: + if not all((args.candidate, args.validation_candidate, args.output)): + parser.error('export requires --candidate --validation-candidate --output') + result = export_inventory(json.loads(args.inventory.read_text()), args.candidate, + args.validation_candidate, args.output, args.base, + json.loads(args.remote_videos.read_text()) if args.remote_videos else None) + print(json.dumps({'episodes': len(result['episodes']), 'counts': result['counts'], + 'errors': result['errors']}, indent=2)) + return bool(result['errors']) + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/action_repair_tools/twm/scripts/publish_action_repairs.py b/action_repair_tools/twm/scripts/publish_action_repairs.py new file mode 100644 index 0000000000000000000000000000000000000000..e97ef017ab3926c948c5a628e6fda48a9c9d4fac --- /dev/null +++ b/action_repair_tools/twm/scripts/publish_action_repairs.py @@ -0,0 +1,133 @@ +"""Merge concurrently rebuilt force into staged actions, then publish explicitly. + +The force base must be the verified export of the immediately preceding HF +commit. A parent-commit guard prevents replacing a newer remote update. +""" +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import pyarrow as pa +import pyarrow.parquet as pq + +from twm.scripts.export_old_motherboard_actions import ( + _atomic_parquet, _columns_equal, _sha256) + + +def _derived(name: str) -> bool: + return (name == 'action' or name.startswith(('action_', 'pose_left_', 'pose_right_')) + or name in ('sensor_left_pose_repaired', 'sensor_right_pose_repaired')) + + +def merge_force_table(action: pa.Table, force: pa.Table) -> pa.Table: + if action.num_rows != force.num_rows: + raise ValueError('force/action row counts differ') + # Check every raw field, not only row count; old action fields in the force + # base are intentionally ignored, since these are what this update replaces. + for name in force.column_names: + if name.startswith('force_') or _derived(name): + continue + if name not in action.column_names or not _columns_equal(action[name], force[name]): + raise ValueError(f'force/action source mismatch: {name}') + result = action + for name in force.column_names: + if not name.startswith('force_'): + continue + field = force.schema.field(name) + if name in result.column_names: + result = result.set_column(result.schema.get_field_index(name), field, force[name]) + else: + result = result.append_column(field, force[name]) + metadata = dict(action.schema.metadata or {}) + for key, value in (force.schema.metadata or {}).items(): + if key.startswith(b'twm.force'): + metadata[key] = value + return result.replace_schema_metadata(metadata) + + +def merge_force_tree(root: Path, force_root: Path, force_revision: str) -> dict: + path = root / 'action_publication_manifest.json' + manifest = json.loads(path.read_text()) + records = {entry['remote_path']: entry for entry in manifest['episodes']} + sources = sorted(force_root.glob('*/meta/*/*.parquet')) + if len(sources) != 36: + raise ValueError(f'expected36 force episodes, found {len(sources)}') + receipt = json.loads((force_root / 'force_commit.json').read_text()) + if receipt.get('revision') != force_revision: + raise ValueError('force export receipt revision mismatch') + expected_hashes = receipt.get('parquet_sha256', {}) + if len(expected_hashes) != 36: + raise ValueError('force receipt must bind36 uploaded parquets') + for source in sources: + if expected_hashes.get(str(source.relative_to(force_root))) != _sha256(source): + raise ValueError(f'force export differs from uploaded content: {source}') + for source in sources: + relative = Path('old_data') / source.relative_to(force_root) + if str(relative) not in records: + raise ValueError(f'missing staged action: {relative}') + dest = root / relative + merged = merge_force_table(pq.read_table(dest), pq.read_table(source)) + _atomic_parquet(merged, dest) + records[str(relative)]['output_sha256'] = _sha256(dest) + manifest['force_revision'] = force_revision + manifest['force_merged_episodes'] = len(sources) + path.write_text(json.dumps(manifest, indent=2) + '\n') + return manifest + + +def validate_publication_manifest(manifest: dict, expected_revision: str) -> None: + if manifest.get('force_revision') != expected_revision: + raise ValueError('force revision must equal the immediately preceding HF commit') + if manifest.get('force_merged_episodes') != 36: + raise ValueError('publication requires36 merged force episodes') + + +def publish(root: Path, expected_revision: str, repo: str = 'yxma/React'): + from huggingface_hub import HfApi, CommitOperationAdd + api = HfApi() + actual = api.repo_info(repo, repo_type='dataset').sha + if actual != expected_revision: + raise ValueError(f'remote changed: expected {expected_revision}, got {actual}') + manifest = json.loads((root / 'action_publication_manifest.json').read_text()) + validate_publication_manifest(manifest, expected_revision) + for entry in manifest['episodes']: + if _sha256(root / entry['remote_path']) != entry['output_sha256']: + raise ValueError(f'staged parquet changed: {entry["remote_path"]}') + files = sorted(p for p in root.rglob('*') if p.is_file()) + top_files = {'action_publication_manifest.json', 'action_sample25.json', + 'action_sample25.html', 'action_review.html', + 'ACTION_REPAIR_20260918.md', 'sample25_verification.json'} + directories = {'data', 'old_data', 'action_repair_tools', 'repair_audit'} + suffixes = {'.parquet', '.npz', '.json', '.html', '.md', '.py', '.txt', '.toml'} + for file in files: + relative = file.relative_to(root) + if (file.suffix not in suffixes or + (len(relative.parts) == 1 and relative.name not in top_files) or + (len(relative.parts) > 1 and relative.parts[0] not in directories)): + raise ValueError(f'unexpected publication artifact: {relative}') + operations = [CommitOperationAdd(path_in_repo=str(p.relative_to(root)), + path_or_fileobj=str(p)) for p in files] + result = api.create_commit(repo, repo_type='dataset', operations=operations, + commit_message='Repair mocap actions across all tasks, old_data and validation; retain V8 force', + parent_commit=expected_revision, num_threads=8) + print(result, flush=True) + return result + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--root', type=Path, required=True) + parser.add_argument('--force-root', type=Path) + parser.add_argument('--parent', required=True) + parser.add_argument('--publish', action='store_true') + args = parser.parse_args() + if args.force_root: + merge_force_tree(args.root, args.force_root, args.parent) + if args.publish: + publish(args.root, args.parent) + + +if __name__ == '__main__': + main() diff --git a/action_repair_tools/twm/scripts/review_mocap_branches.py b/action_repair_tools/twm/scripts/review_mocap_branches.py new file mode 100644 index 0000000000000000000000000000000000000000..a32bec7dfc10620abe45d273ef883e9fe0f5aae3 --- /dev/null +++ b/action_repair_tools/twm/scripts/review_mocap_branches.py @@ -0,0 +1,82 @@ +"""Write separate, never-training-valid branch hypotheses for human review.""" +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np +import pyarrow.parquet as pq + +from twm.react_preprocess.mocap_branch import find_branch_candidates +from twm.scripts.build_mocap_repair_candidates import _atomic_json + + +def write_branch_review(candidate_root, source_root, output_root, *, manifest_digest): + candidate_root, source_root, output_root = map(Path, (candidate_root, source_root, output_root)) + for protected in (candidate_root.resolve(), source_root.resolve()): + if output_root.resolve().is_relative_to(protected) or protected.is_relative_to(output_root.resolve()): + raise ValueError('review output must be separate from source and primary candidates') + proposals = [] + unique_events = set() + gaps = {} + for event_path in sorted(candidate_root.glob('*/repair_events/*/*.json')): + record = json.loads(event_path.read_text()) + unresolved = [e for e in record['events'] if e['confidence'] != 'HIGH'] + if not unresolved: + continue + task = record['task'] + if task not in gaps: + path = source_root/task/'pose_gaps.json' + gaps[task] = json.loads(path.read_text()) if path.exists() else {} + source_path = source_root/record['source_path'] + columns = [f'sensor_{side}_pose' for side in sorted({e['side'] for e in unresolved})] + table = pq.read_table(source_path, columns=columns, use_threads=False) + for side in sorted({e['side'] for e in unresolved}): + raw = np.asarray(table[f'sensor_{side}_pose'].to_pylist(), dtype=float) + known = gaps[task].get(record['date']+'/'+record['episode'], {}).get(side, []) + for proposal in find_branch_candidates(raw, known): + events = [e for e in unresolved if e['side'] == side and + e['start'] <= proposal.end+1 and e['end'] >= proposal.start-1] + if not events: + continue + path = Path(task)/record['date']/f"{record['episode']}_{side}_{proposal.start}_{proposal.end}.json" + overlap_ids = [e['event_id'] for e in events] + unique_events.update((str(event_path), eid) for eid in overlap_ids) + payload = { + 'schema_version': 1, 'manifest_digest': manifest_digest, + 'source_path': record['source_path'], 'source_sha256': record.get('source_sha256'), + 'task': task, 'date': record['date'], 'episode': record['episode'], 'side': side, + 'start': proposal.start, 'end': proposal.end, + 'confidence': 'MEDIUM', 'training_valid': False, + 'requires_independent_branch_identity_evidence': True, + 'overlapping_event_ids': overlap_ids, 'evidence': proposal.evidence, + 'branch_rotation_xyzw': proposal.branch_rotation_xyzw.tolist(), + 'branch_translation_m': proposal.branch_translation_m.tolist(), + 'proposed_pose': proposal.pose.tolist(), + } + _atomic_json(output_root/path, payload) + proposals.append({'path': str(path), 'task': task, 'side': side, + 'start': proposal.start, 'end': proposal.end, + 'matching_return_pairs': proposal.evidence['matching_return_pairs']}) + report = {'schema_version': 1, 'manifest_digest': manifest_digest, + 'proposal_count': len(proposals), 'overlapping_unresolved_events': len(unique_events), + 'training_valid': False, 'requires_independent_branch_identity_evidence': True, + 'proposals': proposals} + _atomic_json(output_root/'index.json', report) + return report + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--candidate-root', type=Path, required=True) + parser.add_argument('--output-root', type=Path, required=True) + args = parser.parse_args() + manifest = json.loads((args.candidate_root/'input_manifest.json').read_text()) + report = write_branch_review(args.candidate_root, manifest['source_root'], args.output_root, + manifest_digest=manifest['digest']) + print(json.dumps({k:v for k,v in report.items() if k != 'proposals'}), flush=True) + + +if __name__ == '__main__': + main() diff --git a/action_review.html b/action_review.html new file mode 100644 index 0000000000000000000000000000000000000000..c5335e33ebdbae30685e9bcdd505afc747630e55 --- /dev/null +++ b/action_review.html @@ -0,0 +1,13156 @@ +Mocap event review

Mocap event review

399 non-HIGH event occurrences across published segments. Raw traces are red; repaired traces are blue. Missing values are gaps. Each chart includes one second of context; long intervals are sampled. Video links and charts use segment-local frame / 30 fps; capture-time offsets are listed separately. No approval is implied. The measured camera footage is unchanged; before/after repair is shown in the red/blue pose charts. These examples support human verification and are not proof that every jump is tracking loss. Check visible hand/object motion against the measured jump, the surrounding frames, and the repair.

data/motherboard/meta/2026-09-11/episode_003_seg00.parquet::right:6919-6920:branch_discontinuity — MEDIUM — endpoint_se3 — 223.367–223.400s

view_middlewrist_rightview_leftview_right

x (mm)440.61292.86y (mm)-265.93-353.13z (mm)129.3271.12step (deg)18.020.21222.37s224.40s
{
+  "source_start_row": 6919,
+  "source_end_row": 6920,
+  "local_start_row": 6701,
+  "local_end_row": 6702,
+  "capture_start_seconds": 226.30533981323242,
+  "capture_end_seconds": 226.33783555030823,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 47.800997433372316,
+    "anchor_translation_mm": 63.09745055264558,
+    "branch_unambiguous": true,
+    "duration_frames": 2,
+    "endpoint_return_consistent": false,
+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 6918,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 2,
+    "physical": true,
+    "prediction_rotation_max_deg": 15.933665811124106,
+    "prediction_translation_max_mm": 21.032483517548528,
+    "reconstructed_max_step_rotation_deg": 18.02254004629295,
+    "reconstructed_max_step_translation_mm": 23.619626470526953,
+    "replaced_frames": 0,
+    "right_anchor": 6921,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 10.882346383786663,
+    "rotation_residual_p95_deg": 3.8708448926461196,
+    "translation_inlier_threshold_mm": 12.887418899887527,
+    "translation_residual_p95_mm": 8.0939798311343
+  }
+}
data/motherboard/meta/2026-09-11/episode_005_seg00.parquet::right:988-1010:known_gap — LOW — unrepaired_ambiguous_branch — 32.933–33.667s

view_middlewrist_rightview_leftview_right

x (mm)452.89410.81y (mm)-275.68-322.98z (mm)151.9395.18step (deg)74.480.0931.93s34.67s
{
+  "source_start_row": 988,
+  "source_end_row": 1010,
+  "local_start_row": 988,
+  "local_end_row": 1010,
+  "capture_start_seconds": 33.923091411590576,
+  "capture_end_seconds": 34.753862619400024,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "known_gap",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 23,
+    "fit_converged": true,
+    "fit_iterations": 2,
+    "left_anchor": 987,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": true,
+    "prediction_rotation_max_deg": 25.666901571080622,
+    "prediction_translation_max_mm": 81.05492076501436,
+    "reconstructed_max_step_rotation_deg": 0.8517370673452712,
+    "reconstructed_max_step_translation_mm": 2.5927819723783916,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 23,
+    "right_anchor": 1011,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.4168308174733784,
+    "rotation_residual_p95_deg": 73.18756471123928,
+    "translation_inlier_threshold_mm": 4.032219895902639,
+    "translation_residual_p95_mm": 31.845096119858983
+  }
+}
data/motherboard/meta/2026-09-11/episode_006_seg00.parquet::left:135-135:branch_discontinuity — MEDIUM — endpoint_se3 — 0.267–0.267s

view_middlewrist_leftview_leftview_right

x (mm)211.1093.72y (mm)-263.22-317.56z (mm)139.3459.30step (deg)12.910.000.00s1.27s
{
+  "source_start_row": 135,
+  "source_end_row": 135,
+  "local_start_row": 8,
+  "local_end_row": 8,
+  "capture_start_seconds": 0.3642127513885498,
+  "capture_end_seconds": 0.3642127513885498,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 12.907309139387474,
+    "anchor_translation_mm": 45.451282814189234,
+    "branch_unambiguous": false,
+    "duration_frames": 1,
+    "endpoint_return_consistent": true,
+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 134,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": true,
+    "prediction_rotation_max_deg": 6.453654569693737,
+    "prediction_translation_max_mm": 22.725641407094617,
+    "reconstructed_max_step_rotation_deg": 6.453654569693842,
+    "reconstructed_max_step_translation_mm": 22.725641407094617,
+    "replaced_frames": 1,
+    "right_anchor": 136,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 6.153024877535055,
+    "rotation_residual_p95_deg": 6.45365456969375,
+    "translation_inlier_threshold_mm": 17.40523897927769,
+    "translation_residual_p95_mm": 22.725641407094617
+  }
+}
data/motherboard/meta/2026-09-11/episode_007_seg00.parquet::right:544-544:branch_discontinuity — MEDIUM — endpoint_se3 — 18.133–18.133s

view_middlewrist_rightview_leftview_right

x (mm)361.04291.57y (mm)-311.53-354.90z (mm)127.8959.61step (deg)15.030.0017.13s19.13s
{
+  "source_start_row": 544,
+  "source_end_row": 544,
+  "local_start_row": 544,
+  "local_end_row": 544,
+  "capture_start_seconds": 20.011152267456055,
+  "capture_end_seconds": 20.011152267456055,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 14.981120961953765,
+    "anchor_translation_mm": 48.37311204519902,
+    "branch_unambiguous": true,
+    "duration_frames": 1,
+    "endpoint_return_consistent": true,
+    "fit_converged": true,
+    "fit_iterations": 0,
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+}
data/motherboard/meta/2026-09-11/episode_007_seg01.parquet::right:3198-3198:branch_discontinuity — MEDIUM — endpoint_se3 — 44.200–44.200s

view_middlewrist_rightview_leftview_right

x (mm)481.81405.40y (mm)-261.36-311.06z (mm)104.9867.08step (deg)22.860.0043.20s45.20s
{
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+}
data/motherboard/meta/2026-09-11/episode_007_seg01.parquet::right:5362-5362:branch_discontinuity — MEDIUM — endpoint_se3 — 116.333–116.333s

view_middlewrist_rightview_leftview_right

x (mm)504.96460.09y (mm)-69.43-157.81z (mm)124.1787.67step (deg)13.150.13115.33s117.33s
{
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+}
data/motherboard/meta/2026-09-11/episode_007_seg02.parquet::left:9206-9206:branch_discontinuity — MEDIUM — endpoint_se3 — 103.533–103.533s

view_middlewrist_leftview_leftview_right

x (mm)331.59208.27y (mm)-125.88-330.59z (mm)115.3087.43step (deg)13.980.00102.53s104.53s
{
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+}
data/motherboard/meta/2026-09-11/episode_007_seg02.parquet::right:8851-8851:branch_discontinuity — MEDIUM — endpoint_se3 — 91.700–91.700s

view_middlewrist_rightview_leftview_right

x (mm)624.83407.04y (mm)-233.34-328.04z (mm)302.55110.45step (deg)13.400.5190.70s92.70s
{
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+}
data/motherboard/meta/2026-09-11/episode_007_seg04.parquet::left:24053-24053:branch_discontinuity — MEDIUM — endpoint_se3 — 0.800–0.800s

view_middlewrist_leftview_leftview_right

x (mm)204.9420.86y (mm)-67.56-301.53z (mm)184.25106.81step (deg)31.080.000.00s1.80s
{
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+}
data/motherboard/meta/2026-09-11/episode_007_seg04.parquet::left:24381-24421:gap_with_discontinuity — LOW — unrepaired_ambiguous_branch — 11.733–13.067s

view_middlewrist_leftview_leftview_right

x (mm)195.0539.39y (mm)-124.42-459.50z (mm)174.5273.21step (deg)13.910.0010.73s14.07s
{
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+}
data/motherboard/meta/2026-09-11/episode_007_seg04.parquet::left:24474-24474:branch_discontinuity — MEDIUM — endpoint_se3 — 14.833–14.833s

view_middlewrist_leftview_leftview_right

x (mm)135.6646.09y (mm)-169.07-314.10z (mm)184.5291.06step (deg)48.630.0013.83s15.83s
{
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+}
data/motherboard/meta/2026-09-11/episode_007_seg04.parquet::right:24330-24330:branch_discontinuity — MEDIUM — endpoint_se3 — 10.033–10.033s

view_middlewrist_rightview_leftview_right

x (mm)697.81431.16y (mm)-86.22-378.56z (mm)272.11121.43step (deg)18.520.009.03s11.03s
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+}
data/motherboard/meta/2026-09-11/episode_007_seg04.parquet::right:24386-24424:known_gap — LOW — unrepaired_ambiguous_branch — 11.900–13.167s

view_middlewrist_rightview_leftview_right

x (mm)599.19359.71y (mm)-115.72-452.25z (mm)180.0279.27step (deg)14.960.0010.90s14.17s
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+  "kind": "known_gap",
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+}
data/motherboard/meta/2026-09-12/episode_000_seg00.parquet::left:25409-25428:known_gap — LOW — unrepaired_ambiguous_branch — 846.967–847.600s

view_middlewrist_leftview_leftview_right

x (mm)340.97246.25y (mm)-254.30-323.84z (mm)141.4081.76step (deg)60.510.26845.97s848.60s
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+    "translation_residual_p95_mm": 39.63937965121875
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+}
data/motherboard/meta/2026-09-12/episode_000_seg00.parquet::right:18241-18269:known_gap — LOW — unrepaired_ambiguous_branch — 608.033–608.967s

view_middlewrist_rightview_leftview_right

x (mm)435.85427.99y (mm)-360.97-389.03z (mm)82.6770.38step (deg)20.310.08607.03s609.97s
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+    "duration_frames": 29,
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+    "translation_inlier_threshold_mm": 4.795698479997411,
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+}
data/motherboard/meta/2026-09-12/episode_000_seg00.parquet::right:38409-38434:known_gap — LOW — unrepaired_ambiguous_branch — 1280.300–1280.633s

view_middlewrist_rightview_leftview_right

x (mm)514.88417.53y (mm)-236.92-467.34z (mm)338.35102.14step (deg)120.360.131279.30s1280.63s
{
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+  "capture_start_seconds": 1290.6433517932892,
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+  "kind": "known_gap",
+  "evidence": {
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+    "duration_frames": 26,
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+    "fit_iterations": 2,
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+    "reconstructed_max_step_translation_mm": 14.468306121058506,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 26,
+    "right_anchor": 38435,
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+    "rotation_residual_p95_deg": 114.0336509687834,
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+    "translation_residual_p95_mm": 86.87016362479505
+  }
+}
data/pushT/meta/2026-09-10/episode_001_seg01.parquet::left:3274-3276:branch_review — MEDIUM — paired_body_branch_review — 26.933–27.000s

view_middlewrist_leftview_leftview_right

x (mm)331.41321.05y (mm)-379.04-388.24z (mm)118.96100.98step (deg)56.660.0225.93s28.00s
{
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data/pushT/meta/2026-09-10/episode_003_seg01.parquet::left:7921-7921:branch_discontinuity — MEDIUM — endpoint_se3 — 16.000–16.000s

view_middlewrist_leftview_leftview_right

x (mm)425.74316.14y (mm)-51.37-136.22z (mm)149.07106.67step (deg)73.370.1915.00s17.00s
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data/pushT/meta/2026-09-10/episode_003_seg02.parquet::left:10247-10248:branch_discontinuity — MEDIUM — endpoint_se3 — 0.000–0.033s

view_middlewrist_leftview_leftview_right

x (mm)207.48100.14y (mm)-213.62-281.40z (mm)144.40112.48step (deg)122.780.000.00s1.03s
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data/pushT/meta/2026-09-10/episode_003_seg06.parquet::left:27028-27028:branch_review — MEDIUM — paired_body_branch_review — 12.033–12.033s

view_middlewrist_leftview_leftview_right

x (mm)347.46278.27y (mm)-112.52-271.80z (mm)127.1287.05step (deg)41.530.1411.03s13.03s
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data/pushT/meta/2026-09-10/episode_003_seg06.parquet::right:27336-27337:branch_discontinuity — MEDIUM — endpoint_se3 — 22.300–22.333s

view_middlewrist_rightview_leftview_right

x (mm)553.50431.09y (mm)-109.08-254.52z (mm)225.72114.45step (deg)12.870.3421.30s23.33s
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data/pushT/meta/2026-09-10/episode_004_seg00.parquet::left:568-571:branch_discontinuity — MEDIUM — endpoint_se3 — 5.433–5.533s

view_middlewrist_leftview_leftview_right

x (mm)257.64177.25y (mm)-213.76-249.78z (mm)148.93105.79step (deg)101.620.374.43s6.53s
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data/pushT/meta/2026-09-10/episode_004_seg02.parquet::right:10779-10781:branch_discontinuity — MEDIUM — endpoint_se3 — 36.933–37.000s

view_middlewrist_rightview_leftview_right

x (mm)471.48334.62y (mm)-219.85-318.98z (mm)146.8497.77step (deg)122.910.3135.93s38.00s
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data/pushT/meta/2026-09-11/episode_005_seg00.parquet::left:194-194:branch_discontinuity — MEDIUM — endpoint_se3 — 6.467–6.467s

view_middlewrist_leftview_leftview_right

x (mm)322.53192.21y (mm)-197.29-412.42z (mm)190.6954.87step (deg)21.920.005.47s7.47s
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data/pushT/meta/2026-09-12/episode_002_seg03.parquet::left:12002-12002:branch_discontinuity — MEDIUM — endpoint_se3 — 71.433–71.433s

view_middlewrist_leftview_leftview_right

x (mm)392.61272.85y (mm)-235.91-377.75z (mm)107.2186.29step (deg)64.400.2970.43s72.43s
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data/pushT/meta/2026-09-12/episode_002_seg03.parquet::left:12023-12026:branch_discontinuity — MEDIUM — endpoint_se3 — 72.133–72.233s

view_middlewrist_leftview_leftview_right

x (mm)392.61289.00y (mm)-214.20-377.75z (mm)107.2185.57step (deg)64.400.2971.13s73.23s
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data/pushT/meta/2026-09-12/episode_007_seg00.parquet::left:1902-1902:branch_review — MEDIUM — paired_body_branch_review — 12.767–12.767s

view_middlewrist_leftview_leftview_right

x (mm)329.54268.01y (mm)-256.23-359.27z (mm)123.0790.19step (deg)63.610.0511.77s13.77s
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+}
data/pushT/meta/2026-09-12/episode_007_seg00.parquet::right:1902-1902:branch_discontinuity — MEDIUM — endpoint_se3 — 12.767–12.767s

view_middlewrist_rightview_leftview_right

x (mm)511.81369.55y (mm)-205.94-357.24z (mm)110.1787.18step (deg)34.320.3111.77s13.77s
{
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+}
data/pushT/meta/2026-09-15/episode_000_seg06.parquet::left:15089-15089:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 0.100–0.100s

view_middlewrist_leftview_leftview_right

x (mm)136.0678.83y (mm)-225.34-255.09z (mm)97.0189.31step (deg)114.790.130.00s1.10s
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+}
data/pushT/meta/2026-09-15/episode_000_seg07.parquet::left:16403-16407:branch_discontinuity — MEDIUM — endpoint_se3 — 0.867–1.000s

view_middlewrist_leftview_leftview_right

x (mm)137.2798.38y (mm)-233.63-273.85z (mm)100.9887.97step (deg)139.340.000.00s2.00s
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+}
data/rope/meta/2026-09-11/episode_000_seg00.parquet::left:2114-2116:branch_discontinuity — MEDIUM — endpoint_se3 — 70.467–70.533s

view_middlewrist_leftview_leftview_right

x (mm)290.19235.01y (mm)-323.47-383.93z (mm)115.1153.54step (deg)95.500.1569.47s71.53s
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+}
data/rope/meta/2026-09-11/episode_000_seg00.parquet::left:5105-5119:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 170.167–170.633s

view_middlewrist_leftview_leftview_right

x (mm)366.76190.57y (mm)-279.45-448.04z (mm)127.6470.47step (deg)57.720.10169.17s171.63s
{
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+}
data/rope/meta/2026-09-11/episode_000_seg00.parquet::left:816-819:branch_discontinuity — MEDIUM — endpoint_se3 — 27.200–27.300s

view_middlewrist_leftview_leftview_right

x (mm)165.3991.03y (mm)-289.59-320.65z (mm)105.5959.06step (deg)115.720.1026.20s28.30s
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+}
data/rope/meta/2026-09-11/episode_000_seg00.parquet::right:3830-3833:branch_discontinuity — MEDIUM — endpoint_se3 — 127.667–127.767s

view_middlewrist_rightview_leftview_right

x (mm)499.56353.91y (mm)-60.19-258.99z (mm)150.1456.80step (deg)48.190.13126.67s128.77s
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+}
data/rope/meta/2026-09-11/episode_000_seg03.parquet::left:11587-11592:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 19.067–19.233s

view_middlewrist_leftview_leftview_right

x (mm)296.51203.24y (mm)-214.52-352.05z (mm)125.7777.67step (deg)66.710.0018.07s20.23s
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+}
data/rope/meta/2026-09-11/episode_000_seg03.parquet::right:12430-12432:branch_review — MEDIUM — paired_body_branch_review — 47.167–47.233s

view_middlewrist_rightview_leftview_right

x (mm)447.70353.64y (mm)-248.17-331.00z (mm)102.7044.97step (deg)47.420.2046.17s48.23s
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+}
data/rope/meta/2026-09-11/episode_001_seg01.parquet::left:4703-4705:branch_discontinuity — MEDIUM — endpoint_se3 — 24.133–24.200s

view_middlewrist_leftview_leftview_right

x (mm)216.66138.98y (mm)-129.50-196.71z (mm)116.3063.43step (deg)99.680.2723.13s25.20s
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+}
data/rope/meta/2026-09-11/episode_002_seg01.parquet::left:7558-7560:branch_discontinuity — MEDIUM — endpoint_se3 — 16.867–16.933s

view_middlewrist_leftview_leftview_right

x (mm)160.73110.19y (mm)-75.75-158.87z (mm)129.3465.61step (deg)165.450.3215.87s17.93s
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+}
data/rope/meta/2026-09-11/episode_002_seg01.parquet::right:7716-7718:branch_discontinuity — MEDIUM — endpoint_se3 — 22.133–22.200s

view_middlewrist_rightview_leftview_right

x (mm)305.58233.47y (mm)-106.51-176.99z (mm)127.2246.62step (deg)63.430.2621.13s23.20s
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data/rope/meta/2026-09-11/episode_004.parquet::left:7417-7418:branch_review — MEDIUM — paired_body_branch_review — 247.233–247.267s

view_middlewrist_leftview_leftview_right

x (mm)290.29278.87y (mm)-288.25-298.85z (mm)96.6267.23step (deg)55.440.03246.23s248.27s
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data/rope/meta/2026-09-11/episode_006_seg00.parquet::left:3103-3105:branch_review — MEDIUM — paired_body_branch_review — 103.433–103.500s

view_middlewrist_leftview_leftview_right

x (mm)281.94221.15y (mm)-364.35-482.25z (mm)96.7374.48step (deg)72.580.16102.43s104.50s
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data/rope/meta/2026-09-11/episode_007_seg03.parquet::left:13966-13969:branch_discontinuity — MEDIUM — endpoint_se3 — 105.967–106.067s

view_middlewrist_leftview_leftview_right

x (mm)307.1699.27y (mm)-223.69-490.54z (mm)142.5685.79step (deg)64.330.21104.97s107.07s
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data/rope/meta/2026-09-14/episode_000.parquet::left:1643-1646:branch_discontinuity — MEDIUM — endpoint_se3 — 54.767–54.867s

view_middlewrist_leftview_leftview_right

x (mm)318.52217.92y (mm)-268.58-348.83z (mm)134.5080.28step (deg)104.150.4053.77s55.87s
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data/rope/meta/2026-09-14/episode_000.parquet::left:2018-2033:branch_review — MEDIUM — paired_body_branch_review — 67.267–67.767s

view_middlewrist_leftview_leftview_right

x (mm)399.31238.74y (mm)-194.42-466.69z (mm)187.2866.47step (deg)79.510.2066.27s68.77s
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+}
data/rope/meta/2026-09-14/episode_000.parquet::left:929-929:branch_discontinuity — MEDIUM — endpoint_se3 — 30.967–30.967s

view_middlewrist_leftview_leftview_right

x (mm)224.15192.36y (mm)-237.96-309.54z (mm)164.0970.96step (deg)13.180.0029.97s31.97s
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data/rope/meta/2026-09-14/episode_001_seg00.parquet::left:447-454:branch_review — MEDIUM — paired_body_branch_review — 1.533–1.767s

view_middlewrist_leftview_leftview_right

x (mm)367.15230.96y (mm)-328.24-467.38z (mm)120.4753.15step (deg)60.770.070.53s2.77s
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data/rope/meta/2026-09-14/episode_001_seg00.parquet::right:714-714:branch_discontinuity — MEDIUM — endpoint_se3 — 10.433–10.433s

view_middlewrist_rightview_leftview_right

x (mm)443.20299.47y (mm)-257.51-480.52z (mm)206.0082.94step (deg)40.460.009.43s11.43s
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data/rope/meta/2026-09-14/episode_001_seg00.parquet::right:727-727:branch_discontinuity — MEDIUM — endpoint_se3 — 10.867–10.867s

view_middlewrist_rightview_leftview_right

x (mm)443.20299.47y (mm)-253.97-480.52z (mm)206.0078.13step (deg)40.460.009.87s11.87s
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data/rope/meta/2026-09-14/episode_001_seg00.parquet::right:742-742:branch_discontinuity — MEDIUM — endpoint_se3 — 11.367–11.367s

view_middlewrist_rightview_leftview_right

x (mm)443.20299.47y (mm)-236.19-480.52z (mm)206.0078.13step (deg)40.460.0010.37s12.37s
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data/rope/meta/2026-09-14/episode_001_seg00.parquet::right:931-940:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 17.667–17.967s

view_middlewrist_rightview_leftview_right

x (mm)473.54272.90y (mm)-214.55-436.94z (mm)135.3963.55step (deg)37.630.4216.67s18.97s
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data/rope/meta/2026-09-14/episode_002_seg00.parquet::left:3279-3282:branch_discontinuity — MEDIUM — endpoint_se3 — 92.267–92.367s

view_middlewrist_leftview_leftview_right

x (mm)388.94341.32y (mm)-311.13-388.18z (mm)107.6963.67step (deg)85.200.3591.27s93.37s
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data/rope/meta/2026-09-14/episode_002_seg00.parquet::right:2790-2793:branch_discontinuity — MEDIUM — endpoint_se3 — 75.967–76.067s

view_middlewrist_rightview_leftview_right

x (mm)439.53333.74y (mm)-279.88-436.19z (mm)166.6277.54step (deg)44.620.3574.97s77.07s
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data/rope/meta/2026-09-14/episode_002_seg01.parquet::left:5221-5223:branch_discontinuity — MEDIUM — endpoint_se3 — 11.767–11.833s

view_middlewrist_leftview_leftview_right

x (mm)289.30202.37y (mm)-301.01-356.72z (mm)95.6458.32step (deg)53.970.4810.77s12.83s
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data/rope/meta/2026-09-14/episode_002_seg01.parquet::left:6304-6304:branch_review — MEDIUM — paired_body_branch_review — 47.867–47.867s

view_middlewrist_leftview_leftview_right

x (mm)402.88327.03y (mm)-337.12-379.29z (mm)80.7467.25step (deg)37.530.0246.87s48.87s
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data/rope/meta/2026-09-14/episode_002_seg01.parquet::left:7358-7361:branch_discontinuity — MEDIUM — endpoint_se3 — 83.000–83.100s

view_middlewrist_leftview_leftview_right

x (mm)324.28259.41y (mm)-278.97-363.69z (mm)121.4175.50step (deg)104.090.7482.00s84.10s
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data/rope/meta/2026-09-14/episode_002_seg01.parquet::right:5910-5913:branch_review — MEDIUM — paired_body_branch_review — 34.733–34.833s

view_middlewrist_rightview_leftview_right

x (mm)398.98291.21y (mm)-382.47-447.06z (mm)142.4381.03step (deg)47.850.1133.73s35.83s
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data/rope/meta/2026-09-14/episode_002_seg03.parquet::left:13388-13388:branch_discontinuity — MEDIUM — endpoint_se3 — 101.033–101.033s

view_middlewrist_leftview_leftview_right

x (mm)311.83229.55y (mm)-240.12-350.96z (mm)137.3584.94step (deg)15.690.11100.03s102.03s
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data/rope/meta/2026-09-14/episode_002_seg03.parquet::right:11291-11292:branch_review — MEDIUM — paired_body_branch_review — 31.133–31.167s

view_middlewrist_rightview_leftview_right

x (mm)469.62324.93y (mm)-267.51-435.67z (mm)218.5677.24step (deg)50.200.3530.13s32.17s
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data/rope/meta/2026-09-14/episode_002_seg03.parquet::right:13122-13123:branch_discontinuity — MEDIUM — endpoint_se3 — 92.167–92.200s

view_middlewrist_rightview_leftview_right

x (mm)429.12345.60y (mm)-370.80-426.29z (mm)104.2860.32step (deg)45.240.1891.17s93.20s
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+}
data/rope/meta/2026-09-14/episode_002_seg03.parquet::right:13388-13388:branch_discontinuity — MEDIUM — endpoint_se3 — 101.033–101.033s

view_middlewrist_rightview_leftview_right

x (mm)509.21403.02y (mm)-231.52-387.46z (mm)129.5980.49step (deg)13.220.31100.03s102.03s
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data/rope/meta/2026-09-14/episode_002_seg03.parquet::right:14048-14054:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 123.033–123.233s

view_middlewrist_rightview_leftview_right

x (mm)488.17338.51y (mm)-191.12-388.36z (mm)164.1264.34step (deg)43.930.50122.03s124.23s
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data/rope/meta/2026-09-14/episode_002_seg03.parquet::right:14705-14705:branch_discontinuity — MEDIUM — endpoint_se3 — 144.933–144.933s

view_middlewrist_rightview_leftview_right

x (mm)531.28309.97y (mm)-128.23-415.96z (mm)177.7975.14step (deg)13.770.36143.93s145.93s
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data/rope/meta/2026-09-14/episode_002_seg04.parquet::left:15950-15951:branch_discontinuity — MEDIUM — endpoint_se3 — 14.900–14.933s

view_middlewrist_leftview_leftview_right

x (mm)393.83290.08y (mm)-235.77-402.79z (mm)115.9554.14step (deg)77.940.2513.90s15.93s
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data/rope/meta/2026-09-14/episode_002_seg04.parquet::left:16540-16542:branch_discontinuity — MEDIUM — endpoint_se3 — 34.567–34.633s

view_middlewrist_leftview_leftview_right

x (mm)407.58289.20y (mm)-124.82-403.97z (mm)175.2267.01step (deg)76.120.3833.57s35.63s
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+}
data/rope/meta/2026-09-14/episode_002_seg04.parquet::right:15596-15598:branch_discontinuity — MEDIUM — endpoint_se3 — 3.100–3.167s

view_middlewrist_rightview_leftview_right

x (mm)444.83394.89y (mm)-268.59-371.10z (mm)106.8960.28step (deg)57.660.292.10s4.17s
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data/rope/meta/2026-09-14/episode_003_seg00.parquet::left:1205-1205:branch_discontinuity — MEDIUM — endpoint_se3 — 12.633–12.633s

view_middlewrist_leftview_leftview_right

x (mm)387.45267.19y (mm)-251.30-439.92z (mm)128.4073.08step (deg)55.720.2911.63s13.63s
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data/rope/meta/2026-09-14/episode_003_seg00.parquet::left:1840-1840:branch_discontinuity — MEDIUM — endpoint_se3 — 33.800–33.800s

view_middlewrist_leftview_leftview_right

x (mm)259.53174.81y (mm)-219.22-260.49z (mm)113.7653.24step (deg)139.590.0032.80s34.67s
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data/rope/meta/2026-09-14/episode_003_seg00.parquet::right:1025-1027:branch_discontinuity — MEDIUM — endpoint_se3 — 6.633–6.700s

view_middlewrist_rightview_leftview_right

x (mm)449.17309.17y (mm)-215.95-407.19z (mm)185.0581.70step (deg)14.410.365.63s7.70s
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data/rope/meta/2026-09-14/episode_003_seg00.parquet::right:1170-1176:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 11.467–11.667s

view_middlewrist_rightview_leftview_right

x (mm)437.49316.67y (mm)-245.30-408.93z (mm)226.3762.57step (deg)140.990.7510.47s12.67s
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+}
data/rope/meta/2026-09-14/episode_003_seg00.parquet::right:1189-1189:branch_discontinuity — MEDIUM — endpoint_se3 — 12.100–12.100s

view_middlewrist_rightview_leftview_right

x (mm)437.49316.67y (mm)-245.30-408.93z (mm)226.3762.57step (deg)140.990.7511.10s13.10s
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+}
data/rope/meta/2026-09-14/episode_003_seg00.parquet::right:1640-1642:branch_discontinuity — MEDIUM — endpoint_se3 — 27.133–27.200s

view_middlewrist_rightview_leftview_right

x (mm)443.40287.87y (mm)-210.63-428.33z (mm)217.9775.76step (deg)15.860.5126.13s28.20s
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+}
data/rope/meta/2026-09-14/episode_003_seg01.parquet::left:4495-4498:branch_discontinuity — MEDIUM — endpoint_se3 — 87.333–87.433s

view_middlewrist_leftview_leftview_right

x (mm)361.94304.87y (mm)-282.32-350.51z (mm)135.0069.56step (deg)102.580.4086.33s88.43s
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data/rope/meta/2026-09-14/episode_003_seg01.parquet::left:4788-4793:branch_discontinuity — MEDIUM — robust_se3_hermite — 97.100–97.267s

view_middlewrist_leftview_leftview_right

x (mm)381.21305.55y (mm)-265.82-355.62z (mm)123.0576.35step (deg)84.160.2496.10s98.27s
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data/rope/meta/2026-09-14/episode_003_seg01.parquet::left:5211-5211:branch_discontinuity — MEDIUM — endpoint_se3 — 111.200–111.200s

view_middlewrist_leftview_leftview_right

x (mm)357.76318.30y (mm)-353.68-391.69z (mm)113.4181.19step (deg)77.210.18110.20s112.20s
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data/rope/meta/2026-09-14/episode_003_seg01.parquet::right:3536-3537:branch_review — MEDIUM — paired_body_branch_review — 55.367–55.400s

view_middlewrist_rightview_leftview_right

x (mm)394.94370.46y (mm)-354.50-383.24z (mm)98.9771.66step (deg)53.410.1254.37s56.40s
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data/rope/meta/2026-09-14/episode_003_seg01.parquet::right:3960-3964:branch_discontinuity — MEDIUM — endpoint_se3 — 69.500–69.633s

view_middlewrist_rightview_leftview_right

x (mm)447.06368.76y (mm)-243.98-404.63z (mm)125.0671.65step (deg)75.710.3468.50s70.63s
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data/rope/meta/2026-09-14/episode_003_seg01.parquet::right:3979-3981:branch_discontinuity — MEDIUM — endpoint_se3 — 70.133–70.200s

view_middlewrist_rightview_leftview_right

x (mm)447.06370.77y (mm)-364.29-404.63z (mm)97.6871.65step (deg)75.710.2869.13s71.20s
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data/rope/meta/2026-09-14/episode_003_seg01.parquet::right:4406-4407:branch_review — MEDIUM — paired_body_branch_review — 84.367–84.400s

view_middlewrist_rightview_leftview_right

x (mm)417.59337.46y (mm)-312.28-424.87z (mm)86.5469.03step (deg)52.360.1483.37s85.40s
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data/rope/meta/2026-09-14/episode_003_seg02.parquet::right:6692-6692:branch_discontinuity — MEDIUM — endpoint_se3 — 34.733–34.733s

view_middlewrist_rightview_leftview_right

x (mm)420.82311.21y (mm)-202.86-419.26z (mm)170.4175.49step (deg)13.560.0933.73s35.73s
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data/rope/meta/2026-09-14/episode_003_seg03.parquet::right:7791-7792:branch_discontinuity — MEDIUM — endpoint_se3 — 1.333–1.367s

view_middlewrist_rightview_leftview_right

x (mm)458.56337.84y (mm)-222.09-382.72z (mm)103.4776.41step (deg)71.120.510.33s2.37s
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data/rope/meta/2026-09-14/episode_005_seg00.parquet::left:229-233:branch_discontinuity — MEDIUM — endpoint_se3 — 7.633–7.767s

view_middlewrist_leftview_leftview_right

x (mm)163.56-4.57y (mm)-267.12-313.69z (mm)184.9657.17step (deg)38.930.006.63s8.77s
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data/rope/meta/2026-09-14/episode_005_seg00.parquet::left:8619-8622:branch_discontinuity — MEDIUM — endpoint_se3 — 287.300–287.400s

view_middlewrist_leftview_leftview_right

x (mm)262.79202.71y (mm)-120.44-203.57z (mm)121.2763.32step (deg)107.650.22286.30s288.40s
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data/rope/meta/2026-09-14/episode_005_seg03.parquet::left:18428-18430:branch_discontinuity — MEDIUM — endpoint_se3 — 134.400–134.467s

view_middlewrist_leftview_leftview_right

x (mm)207.81163.42y (mm)-266.20-300.69z (mm)98.9458.85step (deg)43.490.13133.40s135.10s
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data/rope/meta/2026-09-14/episode_005_seg04.parquet::left:19030-19032:branch_discontinuity — MEDIUM — endpoint_se3 — 4.833–4.900s

view_middlewrist_leftview_leftview_right

x (mm)306.92115.08y (mm)-248.86-337.57z (mm)121.8281.90step (deg)91.400.103.83s5.90s
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data/rope/meta/2026-09-14/episode_005_seg06.parquet::left:22710-22712:branch_discontinuity — MEDIUM — endpoint_se3 — 19.433–19.500s

view_middlewrist_leftview_leftview_right

x (mm)322.41192.55y (mm)-282.49-362.41z (mm)111.2864.31step (deg)54.440.1418.43s20.50s
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data/rope/meta/2026-09-14/episode_005_seg08.parquet::left:27625-27628:branch_discontinuity — MEDIUM — endpoint_se3 — 78.367–78.467s

view_middlewrist_leftview_leftview_right

x (mm)232.28193.99y (mm)-264.73-320.37z (mm)107.8160.22step (deg)89.990.1377.37s79.47s
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data/rope/meta/2026-09-15/episode_000_seg00.parquet::left:2967-2969:branch_discontinuity — MEDIUM — endpoint_se3 — 29.400–29.467s

view_middlewrist_leftview_leftview_right

x (mm)302.07258.32y (mm)-148.60-215.42z (mm)130.9365.85step (deg)77.100.1428.40s30.47s
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data/rope/meta/2026-09-15/episode_000_seg00.parquet::left:3099-3099:branch_discontinuity — MEDIUM — endpoint_se3 — 33.800–33.800s

view_middlewrist_leftview_leftview_right

x (mm)283.13239.23y (mm)-221.01-258.70z (mm)99.8065.11step (deg)56.280.1832.80s34.80s
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data/rope/meta/2026-09-15/episode_000_seg00.parquet::right:2797-2800:branch_discontinuity — MEDIUM — endpoint_se3 — 23.733–23.833s

view_middlewrist_rightview_leftview_right

x (mm)561.45493.46y (mm)-113.86-181.40z (mm)151.7746.36step (deg)99.730.1122.73s24.83s
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data/rope/meta/2026-09-15/episode_000_seg02.parquet::left:7830-7830:branch_discontinuity — MEDIUM — endpoint_se3 — 1.233–1.233s

view_middlewrist_leftview_leftview_right

x (mm)137.28100.16y (mm)-190.30-281.65z (mm)110.7448.89step (deg)134.570.160.23s2.23s
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data/rope/meta/2026-09-15/episode_000_seg02.parquet::right:8408-8411:branch_discontinuity — MEDIUM — endpoint_se3 — 20.500–20.600s

view_middlewrist_rightview_leftview_right

x (mm)376.42336.76y (mm)-270.79-344.73z (mm)94.5644.63step (deg)39.490.3319.50s21.60s
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data/rope/meta/2026-09-15/episode_001_seg00.parquet::left:2527-2545:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 62.700–63.300s

view_middlewrist_leftview_leftview_right

x (mm)192.04-12.41y (mm)-89.91-263.43z (mm)307.3067.48step (deg)165.860.1661.70s64.17s
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data/rope/meta/2026-09-15/episode_001_seg00.parquet::right:2392-2395:branch_discontinuity — MEDIUM — endpoint_se3 — 58.200–58.300s

view_middlewrist_rightview_leftview_right

x (mm)255.06154.57y (mm)-12.91-105.29z (mm)113.0742.32step (deg)42.000.2157.20s59.30s
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data/rope/meta/2026-09-15/episode_001_seg02.parquet::left:6360-6360:branch_review — MEDIUM — paired_body_branch_review — 49.233–49.233s

view_middlewrist_leftview_leftview_right

x (mm)322.04260.57y (mm)-414.73-471.69z (mm)186.7182.62step (deg)65.670.1148.23s50.23s
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data/rope/meta/2026-09-15/episode_001_seg02.parquet::left:6622-6622:branch_review — MEDIUM — paired_body_branch_review — 57.967–57.967s

view_middlewrist_leftview_leftview_right

x (mm)328.42243.63y (mm)-412.78-457.13z (mm)95.0371.32step (deg)62.290.0956.97s58.97s
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data/rope/meta/2026-09-15/episode_001_seg02.parquet::left:6650-6650:branch_review — MEDIUM — paired_body_branch_review — 58.900–58.900s

view_middlewrist_leftview_leftview_right

x (mm)330.47194.56y (mm)-395.69-457.13z (mm)165.3870.18step (deg)62.290.2157.90s59.63s
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data/rope/meta/2026-09-15/episode_002_seg00.parquet::right:992-992:branch_discontinuity — MEDIUM — endpoint_se3 — 33.067–33.067s

view_middlewrist_rightview_leftview_right

x (mm)406.09348.27y (mm)-112.67-166.39z (mm)106.0770.49step (deg)20.090.4632.07s33.27s
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data/rope/meta/2026-09-16/episode_000_seg00.parquet::left:1413-1417:branch_discontinuity — MEDIUM — endpoint_se3 — 47.100–47.233s

view_middlewrist_leftview_leftview_right

x (mm)417.11332.40y (mm)-175.56-210.62z (mm)120.6358.90step (deg)12.930.0046.10s48.23s
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data/rope/meta/2026-09-16/episode_000_seg00.parquet::right:2111-2113:branch_discontinuity — MEDIUM — endpoint_se3 — 70.367–70.433s

view_middlewrist_rightview_leftview_right

x (mm)369.13337.62y (mm)-410.51-446.27z (mm)119.3457.11step (deg)39.350.1269.37s71.43s
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data/rope/meta/2026-09-16/episode_001_seg00.parquet::left:738-754:branch_discontinuity — MEDIUM — robust_se3_hermite — 0.533–1.067s

view_middlewrist_leftview_leftview_right

x (mm)297.19280.46y (mm)-368.38-409.76z (mm)102.0380.75step (deg)62.360.040.00s2.07s
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data/rope/meta/2026-09-16/episode_001_seg00.parquet::left:765-765:branch_review — MEDIUM — paired_body_branch_review — 1.433–1.433s

view_middlewrist_leftview_leftview_right

x (mm)297.19284.50y (mm)-398.28-409.76z (mm)102.0380.63step (deg)62.360.030.43s2.43s
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data/rope/meta/2026-09-16/episode_001_seg01.parquet::right:7320-7320:branch_discontinuity — MEDIUM — endpoint_se3 — 146.667–146.667s

view_middlewrist_rightview_leftview_right

x (mm)500.49360.25y (mm)-133.08-195.23z (mm)135.0952.59step (deg)46.500.11145.67s147.67s
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data/rope/meta/2026-09-16/episode_001_seg01.parquet::right:8749-8753:branch_discontinuity — MEDIUM — endpoint_se3 — 194.300–194.367s

view_middlewrist_rightview_leftview_right

x (mm)474.08428.44y (mm)-247.58-303.75z (mm)250.1177.43step (deg)13.830.15193.30s194.37s
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data/rope/meta/2026-09-16/episode_001_seg06.parquet::left:20075-20078:branch_discontinuity — MEDIUM — endpoint_se3 — 45.467–45.567s

view_middlewrist_leftview_leftview_right

x (mm)301.62221.26y (mm)-351.60-392.26z (mm)101.3473.49step (deg)91.610.1244.47s46.57s
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data/toy/meta/2026-09-14/episode_000_seg00.parquet::left:6525-6529:branch_discontinuity — MEDIUM — endpoint_se3 — 33.700–33.700s

view_middlewrist_leftview_leftview_right

x (mm)196.53166.79y (mm)-233.95-261.42z (mm)225.7682.43step (deg)65.180.3332.70s33.70s
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+}
data/toy/meta/2026-09-14/episode_000_seg00.parquet::right:5919-5920:branch_discontinuity — MEDIUM — endpoint_se3 — 13.500–13.533s

view_middlewrist_rightview_leftview_right

x (mm)345.24226.18y (mm)-333.94-503.78z (mm)101.0966.77step (deg)39.200.0812.50s14.53s
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+}
data/toy/meta/2026-09-14/episode_000_seg00.parquet::right:5938-5938:branch_discontinuity — MEDIUM — endpoint_se3 — 14.133–14.133s

view_middlewrist_rightview_leftview_right

x (mm)345.92226.18y (mm)-286.65-503.78z (mm)102.1467.57step (deg)39.200.2813.13s15.13s
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+}
data/toy/meta/2026-09-14/episode_000_seg03.parquet::right:15106-15107:branch_discontinuity — MEDIUM — endpoint_se3 — 57.200–57.233s

view_middlewrist_rightview_leftview_right

x (mm)471.05404.47y (mm)-151.71-162.89z (mm)133.3097.43step (deg)56.330.0056.20s57.23s
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+}
data/toy/meta/2026-09-14/episode_000_seg04.parquet::right:15177-15177:branch_review — MEDIUM — paired_body_branch_review — 2.000–2.000s

view_middlewrist_rightview_leftview_right

x (mm)407.18392.99y (mm)-164.58-203.20z (mm)93.0774.28step (deg)95.430.031.00s3.00s
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+}
data/toy/meta/2026-09-14/episode_000_seg04.parquet::right:15198-15198:branch_discontinuity — MEDIUM — endpoint_se3 — 2.700–2.700s

view_middlewrist_rightview_leftview_right

x (mm)407.18392.99y (mm)-167.81-203.20z (mm)93.0774.28step (deg)95.430.031.70s3.70s
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+}
data/toy/meta/2026-09-14/episode_000_seg04.parquet::right:15434-15434:branch_discontinuity — MEDIUM — endpoint_se3 — 10.567–10.567s

view_middlewrist_rightview_leftview_right

x (mm)480.05375.14y (mm)-201.64-274.93z (mm)131.2572.93step (deg)60.370.129.57s11.57s
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+}
data/toy/meta/2026-09-14/episode_000_seg04.parquet::right:15590-15590:branch_discontinuity — MEDIUM — endpoint_se3 — 15.767–15.767s

view_middlewrist_rightview_leftview_right

x (mm)434.80392.53y (mm)-168.70-207.97z (mm)137.8782.25step (deg)42.140.3714.77s16.77s
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+}
data/toy/meta/2026-09-16/episode_000_seg01.parquet::left:7300-7300:branch_discontinuity — MEDIUM — endpoint_se3 — 15.100–15.100s

view_middlewrist_leftview_leftview_right

x (mm)370.34174.62y (mm)-202.46-355.83z (mm)173.8185.86step (deg)47.600.0014.10s16.10s
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+}
data/validation/meta/2026-09-09/episode_000.parquet::right:4878-4878:branch_discontinuity — MEDIUM — endpoint_se3 — 162.600–162.600s

view_middlewrist_rightview_leftview_right

x (mm)539.14420.63y (mm)-401.38-429.78z (mm)116.7290.80step (deg)12.340.19161.60s163.60s
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+}
data/validation/meta/2026-09-09/episode_001.parquet::right:1394-1397:branch_discontinuity — MEDIUM — endpoint_se3 — 46.467–46.567s

view_middlewrist_rightview_leftview_right

x (mm)459.55382.50y (mm)-296.01-359.18z (mm)136.0879.50step (deg)151.490.0945.47s47.57s
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+}
data/validation/meta/2026-09-09/episode_001.parquet::right:3236-3236:branch_discontinuity — MEDIUM — endpoint_se3 — 107.867–107.867s

view_middlewrist_rightview_leftview_right

x (mm)442.36362.15y (mm)-245.57-284.32z (mm)87.4667.21step (deg)145.050.08106.87s108.87s
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+}
data/validation/meta/2026-09-09/episode_002.parquet::right:1126-1126:branch_discontinuity — MEDIUM — endpoint_se3 — 37.533–37.533s

view_middlewrist_rightview_leftview_right

x (mm)386.32264.85y (mm)-305.66-429.56z (mm)133.7190.79step (deg)17.280.0836.53s38.53s
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+}
data/validation/meta/2026-09-09/episode_002.parquet::right:185-186:branch_review — MEDIUM — paired_body_branch_review — 6.167–6.200s

view_middlewrist_rightview_leftview_right

x (mm)685.71472.45y (mm)-125.71-351.98z (mm)380.9099.40step (deg)151.030.355.17s7.20s
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+}
data/validation/meta/2026-09-09/episode_002.parquet::right:206-208:branch_discontinuity — MEDIUM — endpoint_se3 — 6.867–6.933s

view_middlewrist_rightview_leftview_right

x (mm)685.71444.80y (mm)-115.99-244.88z (mm)380.9090.01step (deg)151.030.355.87s7.93s
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+}
data/validation/meta/2026-09-09/episode_002.parquet::right:3036-3041:branch_discontinuity — MEDIUM — robust_se3_hermite — 101.200–101.367s

view_middlewrist_rightview_leftview_right

x (mm)459.75366.43y (mm)-252.18-301.05z (mm)120.7174.46step (deg)174.690.13100.20s102.37s
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+}
data/validation/meta/2026-09-09/episode_002.parquet::right:3240-3242:branch_discontinuity — MEDIUM — endpoint_se3 — 108.000–108.067s

view_middlewrist_rightview_leftview_right

x (mm)467.89438.26y (mm)-246.72-293.57z (mm)105.9173.33step (deg)171.990.10107.00s109.07s
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+}
data/validation/meta/2026-09-09/episode_002.parquet::right:3255-3255:branch_review — MEDIUM — paired_body_branch_review — 108.500–108.500s

view_middlewrist_rightview_leftview_right

x (mm)467.89432.39y (mm)-242.76-293.57z (mm)105.9173.33step (deg)171.990.12107.50s109.50s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:1034-1034:branch_discontinuity — MEDIUM — endpoint_se3 — 34.467–34.467s

view_middlewrist_leftview_leftview_right

x (mm)160.58147.02y (mm)-170.83-281.38z (mm)214.7493.95step (deg)14.560.0033.47s35.47s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:1098-1098:branch_review — MEDIUM — paired_body_branch_review — 36.600–36.600s

view_middlewrist_leftview_leftview_right

x (mm)185.74151.63y (mm)-240.47-324.14z (mm)224.00206.78step (deg)33.160.3335.60s37.60s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:11196-11196:branch_discontinuity — MEDIUM — endpoint_se3 — 373.200–373.200s

view_middlewrist_leftview_leftview_right

x (mm)112.5923.42y (mm)-268.52-352.67z (mm)213.3797.76step (deg)13.530.00372.20s374.20s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:11336-11336:branch_discontinuity — MEDIUM — endpoint_se3 — 377.867–377.867s

view_middlewrist_leftview_leftview_right

x (mm)99.696.95y (mm)-303.75-333.37z (mm)248.1487.55step (deg)14.140.00376.87s378.87s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:1168-1168:branch_discontinuity — MEDIUM — endpoint_se3 — 38.933–38.933s

view_middlewrist_leftview_leftview_right

x (mm)180.29147.21y (mm)-229.46-303.50z (mm)218.0485.23step (deg)12.030.0037.93s39.93s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:2652-2652:branch_discontinuity — MEDIUM — endpoint_se3 — 88.400–88.400s

view_middlewrist_leftview_leftview_right

x (mm)156.38131.22y (mm)-311.73-332.96z (mm)164.8379.64step (deg)26.940.0087.40s89.40s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:3287-3287:branch_discontinuity — MEDIUM — endpoint_se3 — 109.567–109.567s

view_middlewrist_leftview_leftview_right

x (mm)195.10102.59y (mm)-234.05-262.63z (mm)126.8994.98step (deg)14.170.00108.57s110.57s
{
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:4269-4280:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 142.300–142.667s

view_middlewrist_leftview_leftview_right

x (mm)422.98-31.77y (mm)-61.96-263.12z (mm)557.18195.49step (deg)167.150.00141.30s143.67s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:4298-4298:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 143.267–143.267s

view_middlewrist_leftview_leftview_right

x (mm)409.33-31.77y (mm)-61.96-263.12z (mm)557.18168.58step (deg)167.150.00142.27s144.27s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:4374-4374:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 145.800–145.800s

view_middlewrist_leftview_leftview_right

x (mm)255.43-40.94y (mm)-105.25-391.63z (mm)350.50249.59step (deg)161.470.00144.80s146.80s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:4411-4411:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 147.033–147.033s

view_middlewrist_leftview_leftview_right

x (mm)437.84255.43y (mm)-75.42-145.92z (mm)271.9089.59step (deg)171.160.00146.03s148.03s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:4501-4501:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 150.033–150.033s

view_middlewrist_leftview_leftview_right

x (mm)436.49199.80y (mm)-130.67-247.58z (mm)108.4690.33step (deg)24.800.00149.03s151.03s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:4520-4520:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 150.667–150.667s

view_middlewrist_leftview_leftview_right

x (mm)436.49199.80y (mm)-93.21-240.44z (mm)148.9599.45step (deg)173.120.00149.67s151.67s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:4542-4542:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 151.400–151.400s

view_middlewrist_leftview_leftview_right

x (mm)431.27187.73y (mm)-93.21-159.33z (mm)181.05100.55step (deg)173.120.00150.40s152.40s
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data/validation/meta/2026-09-09/episode_003.parquet::left:4563-4563:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 152.100–152.100s

view_middlewrist_leftview_leftview_right

x (mm)373.97187.73y (mm)-93.21-177.73z (mm)185.62100.60step (deg)173.120.00151.10s153.10s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:4716-4716:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 157.200–157.200s

view_middlewrist_leftview_leftview_right

x (mm)366.96188.65y (mm)-134.84-203.20z (mm)190.21130.41step (deg)173.040.00156.20s158.20s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:4732-4732:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 157.733–157.733s

view_middlewrist_leftview_leftview_right

x (mm)366.96188.65y (mm)-134.84-203.34z (mm)190.21130.41step (deg)173.040.00156.73s158.73s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:5019-5019:branch_discontinuity — MEDIUM — endpoint_se3 — 167.300–167.300s

view_middlewrist_leftview_leftview_right

x (mm)221.3392.80y (mm)-212.85-325.56z (mm)124.6598.70step (deg)13.450.00166.30s168.30s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:5719-5719:branch_discontinuity — MEDIUM — endpoint_se3 — 190.633–190.633s

view_middlewrist_leftview_leftview_right

x (mm)189.56121.09y (mm)-181.72-295.91z (mm)350.74153.13step (deg)13.710.00189.63s191.63s
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data/validation/meta/2026-09-09/episode_003.parquet::left:5864-5874:branch_discontinuity — MEDIUM — robust_se3_hermite — 195.467–195.800s

view_middlewrist_leftview_leftview_right

x (mm)297.07197.79y (mm)-300.66-413.91z (mm)177.1382.92step (deg)150.260.12194.47s196.80s
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data/validation/meta/2026-09-09/episode_003.parquet::left:5888-5898:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 196.267–196.600s

view_middlewrist_leftview_leftview_right

x (mm)297.07193.26y (mm)-285.13-413.91z (mm)149.6482.92step (deg)150.260.12195.27s197.60s
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data/validation/meta/2026-09-09/episode_003.parquet::left:6412-6439:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 213.733–214.633s

view_middlewrist_leftview_leftview_right

x (mm)183.68162.11y (mm)-269.32-362.89z (mm)256.43207.43step (deg)34.210.07212.73s215.63s
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data/validation/meta/2026-09-09/episode_003.parquet::left:6451-6460:branch_review — MEDIUM — paired_body_branch_review — 215.033–215.333s

view_middlewrist_leftview_leftview_right

x (mm)183.68163.82y (mm)-184.28-341.27z (mm)210.59202.76step (deg)34.210.07214.03s216.33s
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data/validation/meta/2026-09-09/episode_003.parquet::left:6657-6657:branch_discontinuity — MEDIUM — endpoint_se3 — 221.900–221.900s

view_middlewrist_leftview_leftview_right

x (mm)269.27243.01y (mm)-298.08-331.85z (mm)212.53192.34step (deg)34.250.07220.90s222.90s
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data/validation/meta/2026-09-09/episode_003.parquet::left:7187-7187:branch_discontinuity — MEDIUM — endpoint_se3 — 239.567–239.567s

view_middlewrist_leftview_leftview_right

x (mm)279.08222.27y (mm)-283.01-365.88z (mm)206.95127.32step (deg)23.400.00238.57s240.57s
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data/validation/meta/2026-09-09/episode_003.parquet::left:8697-8697:branch_discontinuity — MEDIUM — endpoint_se3 — 289.900–289.900s

view_middlewrist_leftview_leftview_right

x (mm)80.2548.01y (mm)-215.10-259.80z (mm)194.5991.17step (deg)22.570.00288.90s290.90s
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data/validation/meta/2026-09-09/episode_003.parquet::right:10099-10099:branch_discontinuity — MEDIUM — endpoint_se3 — 336.633–336.633s

view_middlewrist_rightview_leftview_right

x (mm)395.79190.61y (mm)-341.68-404.81z (mm)143.7295.41step (deg)12.080.24335.63s337.63s
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data/validation/meta/2026-09-09/episode_003.parquet::right:11207-11207:branch_discontinuity — MEDIUM — endpoint_se3 — 373.567–373.567s

view_middlewrist_rightview_leftview_right

x (mm)198.5683.19y (mm)-192.83-308.90z (mm)288.26211.88step (deg)13.130.32372.57s374.57s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::right:2677-2687:branch_review — MEDIUM — paired_body_branch_review — 89.233–89.567s

view_middlewrist_rightview_leftview_right

x (mm)463.27364.52y (mm)-306.74-343.61z (mm)259.40125.01step (deg)125.580.3088.23s90.57s
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data/validation/meta/2026-09-09/episode_003.parquet::right:2770-2778:branch_discontinuity — MEDIUM — robust_se3_hermite — 92.333–92.600s

view_middlewrist_rightview_leftview_right

x (mm)461.24384.85y (mm)-314.80-347.60z (mm)166.94115.85step (deg)117.520.0891.33s93.60s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::right:2930-2946:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 97.667–98.200s

view_middlewrist_rightview_leftview_right

x (mm)419.06292.19y (mm)-318.01-364.67z (mm)185.53104.97step (deg)136.300.0096.67s99.20s
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data/validation/meta/2026-09-09/episode_003.parquet::right:4261-4271:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 142.033–142.367s

view_middlewrist_rightview_leftview_right

x (mm)803.95592.14y (mm)-118.45-242.31z (mm)408.53207.13step (deg)143.820.00141.03s143.37s
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data/validation/meta/2026-09-09/episode_003.parquet::right:4372-4372:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 145.733–145.733s

view_middlewrist_rightview_leftview_right

x (mm)663.59619.79y (mm)-146.85-199.56z (mm)155.0399.66step (deg)135.620.00144.73s146.73s
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data/validation/meta/2026-09-09/episode_003.parquet::right:4386-4386:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 146.200–146.200s

view_middlewrist_rightview_leftview_right

x (mm)663.59619.79y (mm)-157.28-199.56z (mm)131.3499.66step (deg)135.620.00145.20s147.20s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::right:4421-4424:branch_discontinuity — MEDIUM — endpoint_se3 — 147.367–147.467s

view_middlewrist_rightview_leftview_right

x (mm)664.81574.01y (mm)-159.78-195.00z (mm)121.5396.59step (deg)130.510.00146.37s148.47s
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data/validation/meta/2026-09-09/episode_003.parquet::right:5421-5421:branch_discontinuity — MEDIUM — endpoint_se3 — 180.700–180.700s

view_middlewrist_rightview_leftview_right

x (mm)247.23125.41y (mm)-251.36-366.48z (mm)291.62218.34step (deg)13.170.31179.70s181.70s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::right:6971-6971:branch_discontinuity — MEDIUM — endpoint_se3 — 232.367–232.367s

view_middlewrist_rightview_leftview_right

x (mm)451.32393.75y (mm)-239.18-340.83z (mm)198.88167.32step (deg)142.550.09231.37s233.37s
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data/validation/meta/2026-09-09/episode_003.parquet::right:7010-7015:branch_discontinuity — MEDIUM — robust_se3_hermite — 233.667–233.833s

view_middlewrist_rightview_leftview_right

x (mm)433.09409.45y (mm)-239.68-336.74z (mm)198.92160.67step (deg)134.330.00232.67s234.83s
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data/validation/meta/2026-09-09/episode_003.parquet::right:7359-7359:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 245.300–245.300s

view_middlewrist_rightview_leftview_right

x (mm)463.64372.19y (mm)-310.29-358.80z (mm)254.20145.55step (deg)131.980.00244.30s246.30s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::right:7374-7374:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 245.800–245.800s

view_middlewrist_rightview_leftview_right

x (mm)463.64334.23y (mm)-310.29-343.55z (mm)219.27145.55step (deg)131.980.00244.80s246.80s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::right:8708-8708:branch_discontinuity — MEDIUM — endpoint_se3 — 290.267–290.267s

view_middlewrist_rightview_leftview_right

x (mm)221.1588.01y (mm)-208.40-275.01z (mm)303.75203.91step (deg)13.440.16289.27s291.27s
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+}
data/validation/meta/2026-09-09/episode_004.parquet::right:1483-1483:branch_review — MEDIUM — paired_body_branch_review — 49.433–49.433s

view_middlewrist_rightview_leftview_right

x (mm)349.78237.25y (mm)-208.28-281.76z (mm)147.8476.56step (deg)162.170.0548.43s50.43s
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data/validation/meta/2026-09-09/episode_005.parquet::right:1081-1087:branch_discontinuity — MEDIUM — robust_se3_hermite — 36.033–36.233s

view_middlewrist_rightview_leftview_right

x (mm)410.98361.58y (mm)-217.97-349.39z (mm)132.19104.03step (deg)114.830.0335.03s37.23s
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data/validation/meta/2026-09-09/episode_005.parquet::right:798-798:branch_discontinuity — MEDIUM — endpoint_se3 — 26.600–26.600s

view_middlewrist_rightview_leftview_right

x (mm)436.94379.63y (mm)-215.49-309.12z (mm)137.7398.64step (deg)147.640.1425.60s27.60s
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data/validation/meta/2026-09-09/episode_005.parquet::right:814-814:branch_review — MEDIUM — paired_body_branch_review — 27.133–27.133s

view_middlewrist_rightview_leftview_right

x (mm)436.94347.37y (mm)-215.49-309.12z (mm)137.7398.41step (deg)147.640.2826.13s28.13s
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data/validation/meta/2026-09-09/episode_007.parquet::left:1821-1833:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 60.700–61.100s

view_middlewrist_leftview_leftview_right

x (mm)178.04115.25y (mm)-131.33-337.94z (mm)112.6962.04step (deg)145.330.0359.70s62.10s
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data/validation/meta/2026-09-09/episode_007.parquet::right:1513-1513:branch_review — MEDIUM — paired_body_branch_review — 50.433–50.433s

view_middlewrist_rightview_leftview_right

x (mm)385.83335.87y (mm)-236.12-288.33z (mm)113.7997.35step (deg)138.900.0449.43s51.43s
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data/validation/meta/2026-09-09/episode_007.parquet::right:2338-2338:branch_discontinuity — MEDIUM — endpoint_se3 — 77.933–77.933s

view_middlewrist_rightview_leftview_right

x (mm)489.39418.97y (mm)-90.07-157.10z (mm)117.9980.44step (deg)148.840.1676.93s78.93s
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data/validation/meta/2026-09-09/episode_007.parquet::right:4253-4254:branch_review — MEDIUM — paired_body_branch_review — 141.767–141.800s

view_middlewrist_rightview_leftview_right

x (mm)446.08354.19y (mm)-257.83-323.45z (mm)100.8672.93step (deg)152.570.05140.77s142.80s
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+}
data/validation/meta/2026-09-09/episode_007.parquet::right:887-893:branch_review — MEDIUM — paired_body_branch_review — 29.567–29.767s

view_middlewrist_rightview_leftview_right

x (mm)316.49302.89y (mm)-118.14-153.11z (mm)104.8794.84step (deg)161.550.0228.57s30.77s
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+}
data/validation/meta/2026-09-09/episode_008.parquet::left:2291-2293:branch_discontinuity — MEDIUM — endpoint_se3 — 76.367–76.433s

view_middlewrist_leftview_leftview_right

x (mm)358.08324.30y (mm)-265.56-355.11z (mm)124.51103.23step (deg)94.120.1275.37s77.43s
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+    "replaced_frames": 2,
+    "review_required": true,
+    "right_anchor": 2294,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 3.0767578989636015,
+    "rotation_residual_p95_deg": 86.11383459172036,
+    "translation_inlier_threshold_mm": 3.987129815904602,
+    "translation_residual_p95_mm": 29.08617091142749
+  }
+}
data/validation/meta/2026-09-09/episode_008.parquet::left:2307-2307:branch_review — MEDIUM — paired_body_branch_review — 76.900–76.900s

view_middlewrist_leftview_leftview_right

x (mm)358.08279.08y (mm)-244.33-355.11z (mm)141.90103.23step (deg)94.120.1275.90s77.90s
{
+  "source_start_row": 2307,
+  "source_end_row": 2307,
+  "local_start_row": 2307,
+  "local_end_row": 2307,
+  "capture_start_seconds": 77.4980080127716,
+  "capture_end_seconds": 77.4980080127716,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_review",
+  "evidence": {
+    "benchmark_validated": false,
+    "bidirectional_transform_agrees": true,
+    "boundary_prediction_rotation_max_deg": 0.2157891614716775,
+    "boundary_prediction_translation_max_mm": 1.0420321775513446,
+    "context_frames": 5,
+    "duration_frames": 1,
+    "independent_anchor_mask_supplied": false,
+    "left_anchor": 2306,
+    "matching_return_pairs": 1,
+    "method": "paired_body_branch",
+    "physical": true,
+    "proposal_end": 2307,
+    "proposal_start": 2307,
+    "reconstructed_max_step_rotation_deg": 0.5230368297270495,
+    "reconstructed_max_step_translation_mm": 3.426582221781355,
+    "requires_independent_branch_identity_evidence": true,
+    "right_anchor": 2308,
+    "superseded_events_json": "[{\"event_id\": \"left:2307-2307:branch_discontinuity\", \"bout\": {\"side\": \"left\", \"start\": 2307, \"end\": 2307, \"kind\": \"branch_discontinuity\", \"seed_frames\": [2307], \"left_context_end\": 2306, \"right_context_start\": 2308}, \"confidence\": 2, \"method\": \"endpoint_se3\", \"replaced_frames\": [2307], \"evidence\": {\"left_anchor\": 2306, \"right_anchor\": 2308, \"anchor_translation_mm\": 6.466414997595205, \"anchor_rotation_deg\": 1.0211575243015312, \"prediction_translation_max_mm\": 3.2332074987976025, \"prediction_rotation_max_deg\": 0.5105787621507656, \"left_context_frames\": 15, \"right_context_frames\": 15, \"duration_frames\": 1, \"observed_inlier_frames\": 0, \"replaced_frames\": 1, \"translation_residual_p95_mm\": 28.036547536362868, \"rotation_residual_p95_deg\": 82.42229513718189, \"reconstructed_max_step_translation_mm\": 3.2332074987976025, \"reconstructed_max_step_rotation_deg\": 0.5105787621487325, \"physical\": true, \"fit_iterations\": 0, \"fit_converged\": true, \"translation_inlier_threshold_mm\": 4.2388321496659165, \"rotation_inlier_threshold_deg\": 2.0, \"branch_unambiguous\": true, \"endpoint_return_consistent\": true, \"independent_loss_evidence\": false, \"review_required\": true}}]",
+    "training_valid": false,
+    "transform_rotation_disagreement_deg": 0.4315783229433196,
+    "transform_translation_disagreement_mm": 2.1184337794584156
+  }
+}
data/validation/meta/2026-09-09/episode_008.parquet::left:2320-2330:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 77.333–77.667s

view_middlewrist_leftview_leftview_right

x (mm)358.08279.08y (mm)-236.10-355.11z (mm)141.9086.98step (deg)94.120.0076.33s78.67s
{
+  "source_start_row": 2320,
+  "source_end_row": 2330,
+  "local_start_row": 2320,
+  "local_end_row": 2330,
+  "capture_start_seconds": 77.93712830543518,
+  "capture_end_seconds": 78.2696487903595,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 11,
+    "fit_converged": true,
+    "fit_iterations": 2,
+    "left_anchor": 2319,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": true,
+    "prediction_rotation_max_deg": 34.43479627532232,
+    "prediction_translation_max_mm": 108.77543743665531,
+    "reconstructed_max_step_rotation_deg": 3.6190491570962324,
+    "reconstructed_max_step_translation_mm": 11.631285484562223,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 11,
+    "right_anchor": 2331,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 6.516003828226644,
+    "rotation_residual_p95_deg": 96.38901558174615,
+    "translation_inlier_threshold_mm": 8.110054452401362,
+    "translation_residual_p95_mm": 38.37476696072223
+  }
+}
old_data/motherboard/meta/2026-05-10/episode_000.parquet::left:3092-3117:known_gap — LOW — unrepaired_ambiguous_branch — 103.067–103.900s

view_middleview_leftview_right

x (mm)241.98-41.99y (mm)-218.84-332.64z (mm)119.3062.82step (deg)125.300.20102.07s104.90s
{
+  "source_start_row": 3092,
+  "source_end_row": 3117,
+  "local_start_row": 3092,
+  "local_end_row": 3117,
+  "capture_start_seconds": 104.16365361213684,
+  "capture_end_seconds": 105.00584363937378,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "known_gap",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 26,
+    "fit_converged": true,
+    "fit_iterations": 2,
+    "left_anchor": 3091,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": true,
+    "prediction_rotation_max_deg": 34.95681867548271,
+    "prediction_translation_max_mm": 144.72858358985658,
+    "reconstructed_max_step_rotation_deg": 1.579682891234231,
+    "reconstructed_max_step_translation_mm": 6.383182704428653,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 26,
+    "right_anchor": 3118,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 4.0703206240941086,
+    "rotation_residual_p95_deg": 175.9848975133888,
+    "translation_inlier_threshold_mm": 7.447026206159032,
+    "translation_residual_p95_mm": 47.63245006584364
+  }
+}
old_data/motherboard/meta/2026-05-10/episode_000.parquet::left:3992-3995:branch_discontinuity — MEDIUM — endpoint_se3 — 133.067–133.167s

view_middleview_leftview_right

x (mm)232.03109.20y (mm)-249.63-470.10z (mm)202.4071.26step (deg)15.350.04132.07s134.17s
{
+  "source_start_row": 3992,
+  "source_end_row": 3995,
+  "local_start_row": 3992,
+  "local_end_row": 3995,
+  "capture_start_seconds": 134.43512892723083,
+  "capture_end_seconds": 134.53673195838928,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 53.38807263247265,
+    "anchor_translation_mm": 117.47727253227158,
+    "branch_unambiguous": true,
+    "duration_frames": 4,
+    "endpoint_return_consistent": false,
+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 3991,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 4,
+    "physical": true,
+    "prediction_rotation_max_deg": 10.67761452649453,
+    "prediction_translation_max_mm": 23.495454506454315,
+    "reconstructed_max_step_rotation_deg": 15.348568477897642,
+    "reconstructed_max_step_translation_mm": 32.29442155865882,
+    "replaced_frames": 0,
+    "right_anchor": 3996,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 7.535566403543334,
+    "rotation_residual_p95_deg": 3.613545079458643,
+    "translation_inlier_threshold_mm": 12.656384239200918,
+    "translation_residual_p95_mm": 10.79977057303813
+  }
+}
old_data/motherboard/meta/2026-05-10/episode_000.parquet::left:6729-6729:branch_discontinuity — MEDIUM — endpoint_se3 — 224.300–224.300s

view_middleview_leftview_right

x (mm)532.08399.21y (mm)-325.34-438.17z (mm)109.7756.94step (deg)13.960.23223.30s225.30s
{
+  "source_start_row": 6729,
+  "source_end_row": 6729,
+  "local_start_row": 6729,
+  "local_end_row": 6729,
+  "capture_start_seconds": 229.55019879341125,
+  "capture_end_seconds": 229.55019879341125,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 17.220241474591027,
+    "anchor_translation_mm": 36.045768873387715,
+    "branch_unambiguous": true,
+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "independent_loss_evidence": false,
+    "left_anchor": 6728,
+    "left_context_frames": 15,
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+    "physical": true,
+    "prediction_rotation_max_deg": 8.610120737295514,
+    "prediction_translation_max_mm": 18.022884436693857,
+    "reconstructed_max_step_rotation_deg": 8.610120737295372,
+    "reconstructed_max_step_translation_mm": 18.022884436693857,
+    "replaced_frames": 1,
+    "review_required": true,
+    "right_anchor": 6730,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 3.4831919525606474,
+    "rotation_residual_p95_deg": 5.409516149373159,
+    "translation_inlier_threshold_mm": 14.102088716702955,
+    "translation_residual_p95_mm": 11.149368804047716
+  }
+}
old_data/motherboard/meta/2026-05-10/episode_000.parquet::right:5127-5127:branch_discontinuity — MEDIUM — endpoint_se3 — 170.900–170.900s

view_middleview_leftview_right

x (mm)463.85295.90y (mm)-326.25-373.02z (mm)81.3160.67step (deg)35.420.35169.90s171.90s
{
+  "source_start_row": 5127,
+  "source_end_row": 5127,
+  "local_start_row": 5127,
+  "local_end_row": 5127,
+  "capture_start_seconds": 172.56779766082764,
+  "capture_end_seconds": 172.56779766082764,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 15.642024131737923,
+    "branch_unambiguous": true,
+    "duration_frames": 1,
+    "endpoint_return_consistent": false,
+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 5126,
+    "left_context_frames": 15,
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+    "physical": true,
+    "prediction_rotation_max_deg": 16.825098494561843,
+    "prediction_translation_max_mm": 7.8210120658689615,
+    "reconstructed_max_step_rotation_deg": 16.825098494561807,
+    "reconstructed_max_step_translation_mm": 7.8210120658689615,
+    "replaced_frames": 1,
+    "right_anchor": 5128,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 12.0,
+    "rotation_residual_p95_deg": 18.609496538934682,
+    "translation_inlier_threshold_mm": 10.399175362281033,
+    "translation_residual_p95_mm": 6.191514246693828
+  }
+}
old_data/motherboard/meta/2026-05-10/episode_001.parquet::left:1020-1048:known_gap — LOW — unrepaired_ambiguous_branch — 34.000–34.933s

view_middleview_leftview_right

x (mm)259.05-47.19y (mm)-258.33-347.32z (mm)155.9361.40step (deg)131.010.0033.00s35.93s
{
+  "source_start_row": 1020,
+  "source_end_row": 1048,
+  "local_start_row": 1020,
+  "local_end_row": 1048,
+  "capture_start_seconds": 34.26919960975647,
+  "capture_end_seconds": 35.209755659103394,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "known_gap",
+  "evidence": {
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+    "duration_frames": 29,
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+    "fit_iterations": 2,
+    "left_anchor": 1019,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": true,
+    "prediction_rotation_max_deg": 75.80661767926827,
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+    "reconstructed_max_step_translation_mm": 7.643922723047406,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 29,
+    "right_anchor": 1049,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.9371252639640093,
+    "rotation_residual_p95_deg": 174.42949651874213,
+    "translation_inlier_threshold_mm": 9.825344200671665,
+    "translation_residual_p95_mm": 87.77950764367101
+  }
+}
old_data/motherboard/meta/2026-05-10/episode_001.parquet::left:3303-3303:branch_discontinuity — MEDIUM — endpoint_se3 — 110.100–110.100s

view_middleview_leftview_right

x (mm)253.60111.68y (mm)-255.48-321.57z (mm)169.6876.81step (deg)22.620.30109.10s111.10s
{
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+  "local_start_row": 3303,
+  "local_end_row": 3303,
+  "capture_start_seconds": 111.71688556671143,
+  "capture_end_seconds": 111.71688556671143,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 38.84899135211868,
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+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 3302,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 1,
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+    "prediction_rotation_max_deg": 16.059472052448154,
+    "prediction_translation_max_mm": 19.42449567605934,
+    "reconstructed_max_step_rotation_deg": 22.6176224208432,
+    "reconstructed_max_step_translation_mm": 26.42460527192976,
+    "replaced_frames": 0,
+    "right_anchor": 3304,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 7.786862240979,
+    "rotation_residual_p95_deg": 6.637145479455091,
+    "translation_inlier_threshold_mm": 12.713427988328366,
+    "translation_residual_p95_mm": 7.2968904816602915
+  }
+}
old_data/motherboard/meta/2026-05-10/episode_001.parquet::left:5502-5502:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 183.400–183.400s

view_middleview_leftview_right

x (mm)74.52-42.22y (mm)-328.06-420.26z (mm)188.5974.87step (deg)33.430.00182.40s184.40s
{
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+  "source_end_row": 5502,
+  "local_start_row": 5502,
+  "local_end_row": 5502,
+  "capture_start_seconds": 185.97628498077393,
+  "capture_end_seconds": 185.97628498077393,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 125.34442727508038,
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+    "duration_frames": 1,
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+    "fit_iterations": 0,
+    "left_anchor": 5501,
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+    "prediction_translation_max_mm": 62.67221363754019,
+    "reconstructed_max_step_rotation_deg": 16.716792509174727,
+    "reconstructed_max_step_translation_mm": 62.67221363754019,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 5503,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 16.716792509174823,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 62.67221363754019
+  }
+}
old_data/motherboard/meta/2026-05-10/episode_001.parquet::left:5855-5855:branch_discontinuity — MEDIUM — endpoint_se3 — 195.167–195.167s

view_middleview_leftview_right

x (mm)441.01210.34y (mm)-265.89-305.24z (mm)164.2578.99step (deg)16.970.25194.17s196.17s
{
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+  "local_end_row": 5855,
+  "capture_start_seconds": 197.96961498260498,
+  "capture_end_seconds": 197.96961498260498,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 46.628568784448035,
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+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 5854,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 1,
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+    "prediction_rotation_max_deg": 11.211238556770711,
+    "prediction_translation_max_mm": 23.314284392224017,
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+    "reconstructed_max_step_translation_mm": 25.740364630237455,
+    "replaced_frames": 0,
+    "right_anchor": 5856,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 7.6786233793196175,
+    "rotation_residual_p95_deg": 2.1056873727663943,
+    "translation_inlier_threshold_mm": 12.579388107644213,
+    "translation_residual_p95_mm": 2.5666658763377197
+  }
+}
old_data/motherboard/meta/2026-05-10/episode_001.parquet::left:7087-7087:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 236.233–236.233s

view_middleview_leftview_right

x (mm)288.93-44.51y (mm)-179.59-323.99z (mm)108.9817.26step (deg)122.740.00235.23s237.23s
{
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+  "local_start_row": 7087,
+  "local_end_row": 7087,
+  "capture_start_seconds": 239.91807794570923,
+  "capture_end_seconds": 239.91807794570923,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 364.7774348627077,
+    "branch_unambiguous": true,
+    "duration_frames": 1,
+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 7086,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
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+    "prediction_rotation_max_deg": 61.369790267594524,
+    "prediction_translation_max_mm": 182.38871743135385,
+    "reconstructed_max_step_rotation_deg": 61.3697902675945,
+    "reconstructed_max_step_translation_mm": 182.38871743135385,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 7088,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 61.36979026759454,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 182.38871743135385
+  }
+}
old_data/motherboard/meta/2026-05-10/episode_001.parquet::left:7124-7124:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 237.467–237.467s

view_middleview_leftview_right

x (mm)335.96-7.71y (mm)-179.59-322.22z (mm)129.2217.26step (deg)159.640.00236.47s238.47s
{
+  "source_start_row": 7124,
+  "source_end_row": 7124,
+  "local_start_row": 7124,
+  "local_end_row": 7124,
+  "capture_start_seconds": 241.1626296043396,
+  "capture_end_seconds": 241.1626296043396,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 159.6409231099266,
+    "anchor_translation_mm": 82.1978926987488,
+    "branch_unambiguous": true,
+    "duration_frames": 1,
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+    "fit_iterations": 0,
+    "left_anchor": 7123,
+    "left_context_frames": 15,
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+    "physical": false,
+    "prediction_rotation_max_deg": 79.8204615549633,
+    "prediction_translation_max_mm": 41.0989463493744,
+    "reconstructed_max_step_rotation_deg": 79.82046155496327,
+    "reconstructed_max_step_translation_mm": 41.0989463493744,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 7125,
+    "right_context_frames": 15,
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+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 41.0989463493744
+  }
+}
old_data/motherboard/meta/2026-05-10/episode_001.parquet::left:7146-7146:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 238.200–238.200s

view_middleview_leftview_right

x (mm)367.59-7.71y (mm)-179.59-322.22z (mm)153.9420.59step (deg)159.640.00237.20s239.20s
{
+  "source_start_row": 7146,
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+    "reconstructed_max_step_translation_mm": 183.50584503260077,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 7147,
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+    "rotation_inlier_threshold_deg": 2.0,
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+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 183.50584503260077
+  }
+}
old_data/motherboard/meta/2026-05-10/episode_001.parquet::right:3033-3033:branch_discontinuity — MEDIUM — endpoint_se3 — 101.100–101.100s

view_middleview_leftview_right

x (mm)775.97493.07y (mm)-291.50-311.87z (mm)109.0059.32step (deg)15.570.43100.10s102.10s
{
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+    "left_anchor": 3032,
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+    "translation_residual_p95_mm": 8.897137381253188
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+}
old_data/motherboard/meta/2026-05-10/episode_001.parquet::right:5375-5377:branch_discontinuity — MEDIUM — endpoint_se3 — 179.167–179.233s

view_middleview_leftview_right

x (mm)291.1087.24y (mm)-154.10-424.19z (mm)106.9662.63step (deg)14.060.07178.17s180.23s
{
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+    "translation_residual_p95_mm": 0.0
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+}
old_data/motherboard/meta/2026-05-10/episode_001.parquet::right:5425-5425:branch_discontinuity — MEDIUM — endpoint_se3 — 180.833–180.833s

view_middleview_leftview_right

x (mm)397.54259.24y (mm)-95.83-389.16z (mm)121.2563.38step (deg)12.510.13179.83s181.83s
{
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+    "left_anchor": 5424,
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+    "translation_residual_p95_mm": 5.833002068748207
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+}
old_data/motherboard/meta/2026-05-10/episode_001.parquet::right:7073-7094:known_gap — LOW — unrepaired_ambiguous_branch — 235.767–236.467s

view_middleview_leftview_right

x (mm)622.22-17.64y (mm)-179.04-329.87z (mm)189.1870.32step (deg)32.960.20234.77s237.47s
{
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+  "kind": "known_gap",
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+    "translation_residual_p95_mm": 92.31095957242022
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+}
old_data/motherboard/meta/2026-05-10/episode_001.parquet::right:7561-7561:branch_discontinuity — MEDIUM — endpoint_se3 — 252.033–252.033s

view_middleview_leftview_right

x (mm)615.08163.96y (mm)-267.92-351.36z (mm)147.7553.04step (deg)12.590.05251.03s253.03s
{
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+    "translation_residual_p95_mm": 4.165122451643571
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+}
old_data/motherboard/meta/2026-05-10/episode_001.parquet::right:7604-7604:branch_discontinuity — MEDIUM — endpoint_se3 — 253.467–253.467s

view_middleview_leftview_right

x (mm)318.99163.96y (mm)-144.10-317.93z (mm)148.3152.34step (deg)14.100.37252.47s254.00s
{
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+    "translation_residual_p95_mm": 7.325601579829923
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:10094-10094:branch_discontinuity — MEDIUM — endpoint_se3 — 336.467–336.467s

view_middleview_leftview_right

x (mm)242.84106.36y (mm)-198.42-364.35z (mm)164.2371.84step (deg)24.980.08335.47s337.47s
{
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+    "translation_residual_p95_mm": 25.23018760767675
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:10314-10314:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 343.800–343.800s

view_middleview_leftview_right

x (mm)637.68262.14y (mm)-305.13-353.58z (mm)138.8375.93step (deg)33.680.04342.80s344.80s
{
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+  "capture_start_seconds": 364.8307478427887,
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+    "translation_residual_p95_mm": 99.5821831046109
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:10364-10364:branch_discontinuity — MEDIUM — endpoint_se3 — 345.467–345.467s

view_middleview_leftview_right

x (mm)590.4164.60y (mm)-284.25-329.98z (mm)158.2080.60step (deg)12.450.17344.47s346.47s
{
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+  "capture_start_seconds": 367.05086517333984,
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:117-137:known_gap — LOW — unrepaired_ambiguous_branch — 3.900–4.567s

view_middleview_leftview_right

x (mm)199.662.67y (mm)-233.73-318.50z (mm)184.6157.28step (deg)15.410.292.90s5.57s
{
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+    "replaced_frames": 21,
+    "right_anchor": 138,
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+    "rotation_inlier_threshold_deg": 12.0,
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:360-360:branch_discontinuity — MEDIUM — endpoint_se3 — 12.000–12.000s

view_middleview_leftview_right

x (mm)324.61179.97y (mm)-260.06-349.00z (mm)146.6758.34step (deg)15.850.6811.00s13.00s
{
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+    "left_anchor": 359,
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:6370-6370:branch_discontinuity — MEDIUM — endpoint_se3 — 212.333–212.333s

view_middleview_leftview_right

x (mm)509.2693.58y (mm)-270.79-289.68z (mm)162.0760.36step (deg)12.130.23211.33s213.33s
{
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+  "capture_start_seconds": 216.1158525943756,
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+  "evidence": {
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+    "prediction_translation_max_mm": 31.560731017398272,
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+    "reconstructed_max_step_translation_mm": 34.60133544844921,
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+    "right_anchor": 6371,
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+    "translation_residual_p95_mm": 5.633398599490092
+  }
+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:6619-6619:branch_discontinuity — MEDIUM — endpoint_se3 — 220.633–220.633s

view_middleview_leftview_right

x (mm)369.4857.10y (mm)-290.51-354.24z (mm)160.3366.73step (deg)13.570.11219.63s221.63s
{
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+    "fit_iterations": 0,
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+    "translation_residual_p95_mm": 5.24343444781964
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:8982-8982:branch_discontinuity — MEDIUM — endpoint_se3 — 299.400–299.400s

view_middleview_leftview_right

x (mm)588.8526.15y (mm)-278.62-351.42z (mm)185.0065.93step (deg)12.400.27298.40s300.40s
{
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+    "rotation_residual_p95_deg": 1.8716764730085511,
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+    "translation_residual_p95_mm": 4.266867856102092
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:9024-9024:branch_discontinuity — MEDIUM — endpoint_se3 — 300.800–300.800s

view_middleview_leftview_right

x (mm)201.0422.98y (mm)-278.68-351.87z (mm)185.00102.64step (deg)29.840.17299.80s301.80s
{
+  "source_start_row": 9024,
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+    "fit_iterations": 0,
+    "left_anchor": 9023,
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+    "translation_residual_p95_mm": 27.662447516979302
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:9205-9237:known_gap — LOW — unrepaired_ambiguous_branch — 306.833–307.900s

view_middleview_leftview_right

x (mm)592.8595.37y (mm)-203.93-319.25z (mm)170.7778.25step (deg)25.530.04305.83s308.90s
{
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+  "kind": "known_gap",
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:9294-9294:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 309.800–309.800s

view_middleview_leftview_right

x (mm)389.70226.21y (mm)-194.63-266.17z (mm)122.8574.77step (deg)14.820.33308.80s310.80s
{
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+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
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+    "translation_residual_p95_mm": 59.13134829234843
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:951-951:branch_discontinuity — MEDIUM — endpoint_se3 — 31.700–31.700s

view_middleview_leftview_right

x (mm)410.14226.11y (mm)-236.37-538.36z (mm)139.8772.84step (deg)13.210.1930.70s32.70s
{
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+    "rotation_residual_p95_deg": 2.5874765187307722,
+    "translation_inlier_threshold_mm": 20.0,
+    "translation_residual_p95_mm": 4.325887782069418
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:9510-9510:branch_discontinuity — MEDIUM — endpoint_se3 — 317.000–317.000s

view_middleview_leftview_right

x (mm)476.9744.48y (mm)-238.01-335.60z (mm)167.1673.25step (deg)12.640.35316.00s318.00s
{
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:9674-9674:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 322.467–322.467s

view_middleview_leftview_right

x (mm)541.11148.28y (mm)-258.58-289.57z (mm)174.4773.16step (deg)39.080.19321.47s323.47s
{
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:9995-9995:branch_discontinuity — MEDIUM — endpoint_se3 — 333.167–333.167s

view_middleview_leftview_right

x (mm)575.07368.21y (mm)-215.36-303.29z (mm)128.1863.45step (deg)16.820.32332.17s334.17s
{
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::right:10095-10095:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 336.500–336.500s

view_middleview_leftview_right

x (mm)478.06233.96y (mm)-186.44-365.26z (mm)171.2267.87step (deg)13.180.36335.50s337.50s
{
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+    "replaced_frames": 0,
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::right:10244-10244:branch_discontinuity — MEDIUM — endpoint_se3 — 341.467–341.467s

view_middleview_leftview_right

x (mm)657.50433.84y (mm)-248.85-324.92z (mm)154.3983.64step (deg)13.450.30340.47s342.47s
{
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::right:10315-10315:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 343.833–343.833s

view_middleview_leftview_right

x (mm)711.90421.21y (mm)-249.04-289.51z (mm)173.21105.37step (deg)22.430.09342.83s344.83s
{
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+  "capture_start_seconds": 365.1669797897339,
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+    "left_anchor": 10314,
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+    "reconstructed_max_step_translation_mm": 106.9706111138805,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 10316,
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+    "translation_residual_p95_mm": 99.74342582167696
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::right:158-158:branch_discontinuity — MEDIUM — endpoint_se3 — 5.267–5.267s

view_middleview_leftview_right

x (mm)612.23477.52y (mm)-293.34-340.68z (mm)189.0980.98step (deg)17.620.244.27s6.27s
{
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::right:9015-9024:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 300.500–300.800s

view_middleview_leftview_right

x (mm)596.24164.45y (mm)-288.65-383.65z (mm)198.6273.13step (deg)16.950.07299.50s301.80s
{
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::right:9204-9204:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 306.800–306.800s

view_middleview_leftview_right

x (mm)703.97535.15y (mm)-228.91-288.37z (mm)172.00102.69step (deg)21.490.03305.80s307.80s
{
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::right:9555-9555:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 318.500–318.500s

view_middleview_leftview_right

x (mm)597.86289.58y (mm)-252.43-300.64z (mm)141.4959.81step (deg)20.810.09317.50s319.50s
{
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::right:9585-9585:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 319.500–319.500s

view_middleview_leftview_right

x (mm)665.65289.58y (mm)-252.43-307.59z (mm)153.0659.81step (deg)20.810.10318.50s320.50s
{
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+}
old_data/motherboard/meta/2026-05-10/episode_002.parquet::right:9675-9675:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 322.500–322.500s

view_middleview_leftview_right

x (mm)692.49252.31y (mm)-298.61-361.47z (mm)152.9990.69step (deg)22.430.08321.50s323.50s
{
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+}
old_data/motherboard/meta/2026-05-10/episode_008.parquet::right:751-773:known_gap — MEDIUM — robust_se3_hermite — 25.033–25.767s

view_middleview_leftview_right

x (mm)501.86439.45y (mm)-359.61-402.43z (mm)114.3084.50step (deg)15.670.0424.03s26.77s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_003.parquet::left:1648-1648:branch_discontinuity — MEDIUM — endpoint_se3 — 54.933–54.933s

view_middleview_leftview_right

x (mm)381.79124.19y (mm)-275.76-423.91z (mm)149.1668.28step (deg)13.750.1953.93s55.93s
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+}
old_data/motherboard/meta/2026-05-11/episode_003.parquet::left:3960-3961:branch_discontinuity — MEDIUM — endpoint_se3 — 132.000–132.033s

view_middleview_leftview_right

x (mm)360.15135.97y (mm)-277.83-458.12z (mm)166.7553.84step (deg)13.760.02131.00s133.03s
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+}
old_data/motherboard/meta/2026-05-11/episode_003.parquet::left:6991-6991:branch_discontinuity — MEDIUM — endpoint_se3 — 233.033–233.033s

view_middleview_leftview_right

x (mm)280.67163.15y (mm)-346.61-492.55z (mm)122.1059.13step (deg)16.180.30232.03s234.03s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_003.parquet::right:3978-3978:branch_discontinuity — MEDIUM — endpoint_se3 — 132.600–132.600s

view_middleview_leftview_right

x (mm)496.06281.57y (mm)-325.46-433.59z (mm)154.8450.28step (deg)12.810.41131.60s133.60s
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+}
old_data/motherboard/meta/2026-05-11/episode_004.parquet::right:1348-1348:branch_discontinuity — MEDIUM — endpoint_se3 — 44.933–44.933s

view_middleview_leftview_right

x (mm)736.85462.18y (mm)-302.06-333.37z (mm)168.9766.59step (deg)13.000.0543.93s45.93s
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+}
old_data/motherboard/meta/2026-05-11/episode_004.parquet::right:406-406:branch_discontinuity — MEDIUM — endpoint_se3 — 13.533–13.533s

view_middleview_leftview_right

x (mm)671.10293.18y (mm)-238.71-310.61z (mm)170.2568.15step (deg)13.660.1012.53s14.53s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_005.parquet::left:12816-12843:known_gap — LOW — unrepaired_ambiguous_branch — 427.200–428.100s

view_middleview_leftview_right

x (mm)320.51-44.89y (mm)-260.48-386.85z (mm)116.8361.77step (deg)174.180.00426.20s429.10s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_005.parquet::left:5395-5416:known_gap — LOW — unrepaired_ambiguous_branch — 179.833–180.533s

view_middleview_leftview_right

x (mm)283.73-35.80y (mm)-241.99-299.51z (mm)121.1459.57step (deg)162.910.00178.83s181.53s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_005.parquet::right:10454-10454:branch_discontinuity — MEDIUM — endpoint_se3 — 348.467–348.467s

view_middleview_leftview_right

x (mm)785.94535.36y (mm)-291.93-553.85z (mm)243.09131.98step (deg)18.620.00347.47s349.47s
{
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+    "translation_residual_p95_mm": 42.693236247578625
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+}
old_data/motherboard/meta/2026-05-11/episode_005.parquet::right:2606-2606:branch_discontinuity — MEDIUM — endpoint_se3 — 86.867–86.867s

view_middleview_leftview_right

x (mm)751.54539.06y (mm)-320.60-544.70z (mm)268.12131.59step (deg)16.080.0085.87s87.87s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_005.parquet::right:567-567:branch_discontinuity — MEDIUM — endpoint_se3 — 18.900–18.900s

view_middleview_leftview_right

x (mm)603.42314.85y (mm)-274.32-326.13z (mm)146.4153.39step (deg)12.260.1817.90s19.90s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_006.parquet::left:8963-8963:branch_discontinuity — MEDIUM — endpoint_se3 — 298.767–298.767s

view_middleview_leftview_right

x (mm)262.5977.08y (mm)-203.65-380.36z (mm)123.8656.59step (deg)14.260.30297.77s299.77s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_007.parquet::left:2728-2728:branch_discontinuity — MEDIUM — endpoint_se3 — 90.933–90.933s

view_middleview_leftview_right

x (mm)391.41192.70y (mm)-226.22-378.78z (mm)141.3885.89step (deg)12.020.1089.93s91.93s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_007.parquet::right:2748-2748:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 91.600–91.600s

view_middleview_leftview_right

x (mm)603.09338.36y (mm)-316.85-357.77z (mm)136.5680.63step (deg)13.580.0990.60s92.60s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_007.parquet::right:2815-2815:branch_discontinuity — MEDIUM — endpoint_se3 — 93.833–93.833s

view_middleview_leftview_right

x (mm)803.49522.20y (mm)-314.37-356.35z (mm)134.3482.27step (deg)13.000.1592.83s94.83s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_011.parquet::left:6239-6239:branch_discontinuity — MEDIUM — endpoint_se3 — 207.967–207.967s

view_middleview_leftview_right

x (mm)354.20233.61y (mm)-341.29-414.45z (mm)181.5093.19step (deg)17.140.02206.97s208.97s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_011.parquet::left:6258-6258:branch_discontinuity — MEDIUM — endpoint_se3 — 208.600–208.600s

view_middleview_leftview_right

x (mm)354.20233.61y (mm)-331.71-414.45z (mm)181.5093.19step (deg)17.140.02207.60s209.60s
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+}
old_data/motherboard/meta/2026-05-11/episode_011.parquet::left:6459-6459:branch_discontinuity — MEDIUM — endpoint_se3 — 215.300–215.300s

view_middleview_leftview_right

x (mm)330.64271.56y (mm)-339.97-382.88z (mm)103.1354.73step (deg)19.980.23214.30s216.30s
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+}
old_data/motherboard/meta/2026-05-11/episode_011.parquet::left:6479-6479:branch_discontinuity — MEDIUM — endpoint_se3 — 215.967–215.967s

view_middleview_leftview_right

x (mm)320.80271.56y (mm)-339.97-382.88z (mm)103.1354.73step (deg)19.980.26214.97s216.97s
{
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+    "translation_residual_p95_mm": 20.38369220154898
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+}
old_data/motherboard/meta/2026-05-11/episode_011.parquet::left:7239-7239:branch_discontinuity — MEDIUM — endpoint_se3 — 241.300–241.300s

view_middleview_leftview_right

x (mm)263.0193.11y (mm)-286.20-333.89z (mm)108.2359.05step (deg)18.510.02240.30s242.30s
{
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+    "translation_residual_p95_mm": 8.94776361623106
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+}
old_data/motherboard/meta/2026-05-11/episode_011.parquet::right:6239-6239:branch_discontinuity — MEDIUM — endpoint_se3 — 207.967–207.967s

view_middleview_leftview_right

x (mm)430.28292.92y (mm)-353.32-455.12z (mm)139.9866.83step (deg)28.470.16206.97s208.97s
{
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+    "translation_residual_p95_mm": 32.76029042882235
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+}
old_data/motherboard/meta/2026-05-11/episode_011.parquet::right:6259-6259:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 208.633–208.633s

view_middleview_leftview_right

x (mm)430.28292.92y (mm)-353.32-455.12z (mm)139.9869.13step (deg)28.470.16207.63s209.63s
{
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+    "translation_residual_p95_mm": 68.52916061974629
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+}
old_data/motherboard/meta/2026-05-11/episode_011.parquet::right:6489-6489:branch_discontinuity — MEDIUM — endpoint_se3 — 216.300–216.300s

view_middleview_leftview_right

x (mm)476.53429.58y (mm)-351.59-410.01z (mm)116.5469.61step (deg)17.800.07215.30s217.30s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:10088-10109:known_gap — LOW — unrepaired_ambiguous_branch — 336.267–336.967s

view_middleview_leftview_right

x (mm)-24.55-36.61y (mm)-439.21-450.43z (mm)67.7154.51step (deg)24.860.00335.27s337.97s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:10151-10177:known_gap — LOW — unrepaired_ambiguous_branch — 338.367–339.233s

view_middleview_leftview_right

x (mm)-15.63-31.61y (mm)-439.21-471.87z (mm)83.2255.08step (deg)81.090.00337.37s340.23s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:10194-10194:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 339.800–339.800s

view_middleview_leftview_right

x (mm)-11.73-24.10y (mm)-445.37-471.87z (mm)83.2257.93step (deg)81.090.00338.80s340.80s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:11342-11376:known_gap — LOW — unrepaired_ambiguous_branch — 378.067–379.200s

view_middleview_leftview_right

x (mm)47.32-62.36y (mm)-385.60-478.74z (mm)125.9559.39step (deg)115.150.00377.07s380.20s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:11463-11463:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 382.100–382.100s

view_middleview_leftview_right

x (mm)-14.30-42.77y (mm)-449.69-478.55z (mm)96.2768.14step (deg)69.120.00381.10s383.10s
{
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+    "replaced_frames": 1,
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+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:11507-11507:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 383.567–383.567s

view_middleview_leftview_right

x (mm)-29.96-42.77y (mm)-470.65-478.55z (mm)82.9770.78step (deg)71.830.00382.57s384.57s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:12579-12579:branch_discontinuity — MEDIUM — endpoint_se3 — 419.300–419.300s

view_middleview_leftview_right

x (mm)350.69293.44y (mm)-75.64-190.76z (mm)130.7370.51step (deg)12.940.08418.30s420.30s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:5780-5780:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 192.667–192.667s

view_middleview_leftview_right

x (mm)24.84-43.98y (mm)-373.18-497.28z (mm)164.3957.51step (deg)110.180.00191.67s193.67s
{
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+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:5802-5853:known_gap — LOW — unrepaired_ambiguous_branch — 193.400–195.100s

view_middleview_leftview_right

x (mm)55.28-43.59y (mm)-159.83-499.78z (mm)164.3946.33step (deg)161.010.00192.40s196.10s
{
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+  "kind": "known_gap",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 52,
+    "fit_converged": true,
+    "fit_iterations": 2,
+    "left_anchor": 5801,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": true,
+    "prediction_rotation_max_deg": 75.04383142477337,
+    "prediction_translation_max_mm": 201.2946053916627,
+    "reconstructed_max_step_rotation_deg": 2.0133643956083915,
+    "reconstructed_max_step_translation_mm": 5.913753032258238,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 52,
+    "right_anchor": 5854,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 12.0,
+    "rotation_residual_p95_deg": 75.98738196545105,
+    "translation_inlier_threshold_mm": 20.0,
+    "translation_residual_p95_mm": 52.445319964019134
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:5877-5877:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 195.900–195.900s

view_middleview_leftview_right

x (mm)55.28-10.23y (mm)-140.23-365.88z (mm)100.3646.33step (deg)161.010.00194.90s196.90s
{
+  "source_start_row": 5877,
+  "source_end_row": 5877,
+  "local_start_row": 5877,
+  "local_end_row": 5877,
+  "capture_start_seconds": 199.28582382202148,
+  "capture_end_seconds": 199.28582382202148,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 161.013299894793,
+    "anchor_translation_mm": 87.88724722061447,
+    "branch_unambiguous": true,
+    "duration_frames": 1,
+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 5876,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": false,
+    "prediction_rotation_max_deg": 80.5066499473965,
+    "prediction_translation_max_mm": 43.943623610307235,
+    "reconstructed_max_step_rotation_deg": 80.50664994739647,
+    "reconstructed_max_step_translation_mm": 43.943623610307235,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 5878,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 80.5066499473965,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 43.943623610307235
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:5902-5904:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 196.733–196.800s

view_middleview_leftview_right

x (mm)55.28-10.23y (mm)-140.23-210.83z (mm)100.3647.05step (deg)161.010.00195.73s197.80s
{
+  "source_start_row": 5902,
+  "source_end_row": 5904,
+  "local_start_row": 5902,
+  "local_end_row": 5904,
+  "capture_start_seconds": 200.12579464912415,
+  "capture_end_seconds": 200.19189286231995,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 165.05837676579475,
+    "anchor_translation_mm": 28.877907935002348,
+    "branch_unambiguous": true,
+    "duration_frames": 3,
+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 5901,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 3,
+    "physical": false,
+    "prediction_rotation_max_deg": 41.26459419144869,
+    "prediction_translation_max_mm": 7.219476983750587,
+    "reconstructed_max_step_rotation_deg": 41.264594191448666,
+    "reconstructed_max_step_translation_mm": 7.219476983750587,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 0,
+    "right_anchor": 5905,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 2.9108900601956455e-14,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 0.0
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:5946-5946:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 198.200–198.200s

view_middleview_leftview_right

x (mm)50.8150.47y (mm)-115.90-153.88z (mm)100.3680.27step (deg)67.860.00197.20s199.20s
{
+  "source_start_row": 5946,
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+  "local_start_row": 5946,
+  "local_end_row": 5946,
+  "capture_start_seconds": 201.60504961013794,
+  "capture_end_seconds": 201.60504961013794,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 67.86472079107392,
+    "anchor_translation_mm": 42.969710361563315,
+    "branch_unambiguous": true,
+    "duration_frames": 1,
+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 5945,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": false,
+    "prediction_rotation_max_deg": 33.93236039553696,
+    "prediction_translation_max_mm": 21.484855180781658,
+    "reconstructed_max_step_rotation_deg": 33.93236039553688,
+    "reconstructed_max_step_translation_mm": 21.484855180781658,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 5947,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 33.93236039553698,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 21.484855180781658
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:5995-6005:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 199.833–200.167s

view_middleview_leftview_right

x (mm)283.1411.99y (mm)-114.14-126.17z (mm)82.1261.22step (deg)169.980.00198.83s201.17s
{
+  "source_start_row": 5995,
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+  "local_start_row": 5995,
+  "local_end_row": 6005,
+  "capture_start_seconds": 203.24963188171387,
+  "capture_end_seconds": 203.58735728263855,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 11,
+    "fit_converged": true,
+    "fit_iterations": 2,
+    "left_anchor": 5994,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": true,
+    "prediction_rotation_max_deg": 179.71329810064205,
+    "prediction_translation_max_mm": 124.22132885419711,
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+    "reconstructed_max_step_translation_mm": 7.195723636319769,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 11,
+    "right_anchor": 6006,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 3.1637993624372127,
+    "rotation_residual_p95_deg": 163.29532849801808,
+    "translation_inlier_threshold_mm": 8.182037724856624,
+    "translation_residual_p95_mm": 52.44600022108973
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:7708-7737:gap_with_discontinuity — LOW — unrepaired_ambiguous_branch — 256.933–257.900s

view_middleview_leftview_right

x (mm)45.35-47.56y (mm)-451.93-507.60z (mm)138.3851.17step (deg)169.410.00255.93s258.90s
{
+  "source_start_row": 7708,
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+  "local_start_row": 7708,
+  "local_end_row": 7737,
+  "capture_start_seconds": 261.47991609573364,
+  "capture_end_seconds": 262.45116353034973,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "gap_with_discontinuity",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 30,
+    "fit_converged": true,
+    "fit_iterations": 2,
+    "left_anchor": 7707,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": true,
+    "prediction_rotation_max_deg": 26.769306980806345,
+    "prediction_translation_max_mm": 72.971750738771,
+    "reconstructed_max_step_rotation_deg": 0.9958612642752535,
+    "reconstructed_max_step_translation_mm": 3.4623766618927765,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 30,
+    "right_anchor": 7738,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 3.258690183899019,
+    "rotation_residual_p95_deg": 91.91825795478442,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 78.5596738957753
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:7806-7826:known_gap — LOW — unrepaired_ambiguous_branch — 260.200–260.867s

view_middleview_leftview_right

x (mm)46.32-36.32y (mm)-473.15-531.68z (mm)140.9870.42step (deg)104.690.00259.20s261.87s
{
+  "source_start_row": 7806,
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+  "local_end_row": 7826,
+  "capture_start_seconds": 264.8834283351898,
+  "capture_end_seconds": 265.5758411884308,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "known_gap",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 21,
+    "fit_converged": true,
+    "fit_iterations": 2,
+    "left_anchor": 7805,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": true,
+    "prediction_rotation_max_deg": 13.398471225404872,
+    "prediction_translation_max_mm": 99.31131440621024,
+    "reconstructed_max_step_rotation_deg": 0.8813280101748657,
+    "reconstructed_max_step_translation_mm": 6.6273612751525555,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 21,
+    "right_anchor": 7827,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 11.899470865471187,
+    "rotation_residual_p95_deg": 34.02805475156893,
+    "translation_inlier_threshold_mm": 12.660391051001271,
+    "translation_residual_p95_mm": 54.44347074100932
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:7899-7948:known_gap — LOW — unrepaired_ambiguous_branch — 263.300–264.933s

view_middleview_leftview_right

x (mm)85.23-16.51y (mm)-467.18-508.08z (mm)125.2656.24step (deg)97.590.00262.30s265.93s
{
+  "source_start_row": 7899,
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+  "local_start_row": 7899,
+  "local_end_row": 7948,
+  "capture_start_seconds": 268.02882146835327,
+  "capture_end_seconds": 269.75277280807495,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "known_gap",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 50,
+    "fit_converged": true,
+    "fit_iterations": 2,
+    "left_anchor": 7898,
+    "left_context_frames": 15,
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+    "physical": true,
+    "prediction_rotation_max_deg": 31.23143415609786,
+    "prediction_translation_max_mm": 323.8658673708492,
+    "reconstructed_max_step_rotation_deg": 0.45939234064315443,
+    "reconstructed_max_step_translation_mm": 5.8381546326675515,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 50,
+    "right_anchor": 7949,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 12.0,
+    "rotation_residual_p95_deg": 73.5247173399458,
+    "translation_inlier_threshold_mm": 17.81627185975563,
+    "translation_residual_p95_mm": 123.6278587844893
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:8050-8112:known_gap — LOW — unrepaired_ambiguous_branch — 268.333–270.400s

view_middleview_leftview_right

x (mm)85.1716.24y (mm)-491.70-519.58z (mm)141.3266.39step (deg)136.840.00267.33s271.40s
{
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+  "local_end_row": 8112,
+  "capture_start_seconds": 273.20459961891174,
+  "capture_end_seconds": 275.2894914150238,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "known_gap",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 63,
+    "fit_converged": true,
+    "fit_iterations": 2,
+    "left_anchor": 8049,
+    "left_context_frames": 15,
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+    "physical": true,
+    "prediction_rotation_max_deg": 15.626278070397214,
+    "prediction_translation_max_mm": 178.4082690643574,
+    "reconstructed_max_step_rotation_deg": 0.1938542514576997,
+    "reconstructed_max_step_translation_mm": 2.5265838843893103,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 63,
+    "right_anchor": 8113,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 12.0,
+    "rotation_residual_p95_deg": 79.82063480272399,
+    "translation_inlier_threshold_mm": 11.4550101703306,
+    "translation_residual_p95_mm": 73.02856824947223
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:8165-8186:known_gap — LOW — unrepaired_ambiguous_branch — 272.167–272.867s

view_middleview_leftview_right

x (mm)86.81-14.94y (mm)-486.75-511.15z (mm)126.0763.17step (deg)62.580.00271.17s273.87s
{
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+  "capture_start_seconds": 277.16867685317993,
+  "capture_end_seconds": 277.87176632881165,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "known_gap",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 22,
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+    "fit_iterations": 2,
+    "left_anchor": 8164,
+    "left_context_frames": 15,
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+    "physical": true,
+    "prediction_rotation_max_deg": 16.722649888629455,
+    "prediction_translation_max_mm": 101.77220556146104,
+    "reconstructed_max_step_rotation_deg": 0.9443104979787291,
+    "reconstructed_max_step_translation_mm": 4.1977553234620295,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 22,
+    "right_anchor": 8187,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 12.0,
+    "rotation_residual_p95_deg": 65.42933809259014,
+    "translation_inlier_threshold_mm": 13.206657877971853,
+    "translation_residual_p95_mm": 54.668224693919
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::right:1015-1015:branch_discontinuity — MEDIUM — endpoint_se3 — 33.833–33.833s

view_middleview_leftview_right

x (mm)445.30402.12y (mm)-302.41-370.68z (mm)154.7168.66step (deg)19.090.0932.83s34.83s
{
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+  "local_start_row": 1015,
+  "local_end_row": 1015,
+  "capture_start_seconds": 34.35711121559143,
+  "capture_end_seconds": 34.35711121559143,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 69.77206823246651,
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+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 1014,
+    "left_context_frames": 15,
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+    "physical": true,
+    "prediction_rotation_max_deg": 10.27473418413528,
+    "prediction_translation_max_mm": 34.886034116233255,
+    "reconstructed_max_step_rotation_deg": 10.27473418413528,
+    "reconstructed_max_step_translation_mm": 34.886034116233255,
+    "replaced_frames": 1,
+    "right_anchor": 1016,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 3.2993114968156627,
+    "rotation_residual_p95_deg": 8.83332351615796,
+    "translation_inlier_threshold_mm": 6.594983500948052,
+    "translation_residual_p95_mm": 30.158982033963312
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::right:11770-11770:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 392.333–392.333s

view_middleview_leftview_right

x (mm)531.21415.36y (mm)-403.24-583.48z (mm)435.81111.09step (deg)17.890.00391.33s393.33s
{
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+  "local_start_row": 11770,
+  "local_end_row": 11770,
+  "capture_start_seconds": 401.01555466651917,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 124.77545192299037,
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+    "duration_frames": 1,
+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 11769,
+    "left_context_frames": 15,
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+    "prediction_rotation_max_deg": 8.944248648303747,
+    "prediction_translation_max_mm": 62.387725961495185,
+    "reconstructed_max_step_rotation_deg": 8.944248648303551,
+    "reconstructed_max_step_translation_mm": 62.387725961495185,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 11771,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 8.944248648303793,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 62.387725961495185
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::right:2548-2548:branch_discontinuity — MEDIUM — endpoint_se3 — 84.933–84.933s

view_middleview_leftview_right

x (mm)725.93477.08y (mm)-253.52-369.09z (mm)235.4567.51step (deg)14.510.2483.93s85.93s
{
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+  "capture_start_seconds": 86.87589645385742,
+  "capture_end_seconds": 86.87589645385742,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 77.46678498402353,
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+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 2547,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 1,
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+    "prediction_rotation_max_deg": 13.139128905044025,
+    "prediction_translation_max_mm": 38.73339249201177,
+    "reconstructed_max_step_rotation_deg": 14.513655385964945,
+    "reconstructed_max_step_translation_mm": 42.257832729146955,
+    "replaced_frames": 0,
+    "right_anchor": 2549,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 6.402934183012203,
+    "rotation_residual_p95_deg": 1.4733186076437748,
+    "translation_inlier_threshold_mm": 16.26869175649327,
+    "translation_residual_p95_mm": 4.15251923311146
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_014.parquet::left:2018-2018:branch_discontinuity — MEDIUM — endpoint_se3 — 67.267–67.267s

view_middleview_leftview_right

x (mm)20.79-14.49y (mm)-159.46-428.51z (mm)120.2343.80step (deg)161.400.0066.27s68.27s
{
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+  "local_start_row": 2018,
+  "local_end_row": 2018,
+  "capture_start_seconds": 68.09903931617737,
+  "capture_end_seconds": 68.09903931617737,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 56.22688467543361,
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+    "branch_unambiguous": true,
+    "duration_frames": 1,
+    "endpoint_return_consistent": false,
+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 2017,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": true,
+    "prediction_rotation_max_deg": 28.113442337716805,
+    "prediction_translation_max_mm": 9.486565203489286,
+    "reconstructed_max_step_rotation_deg": 28.113442337716748,
+    "reconstructed_max_step_translation_mm": 9.486565203489286,
+    "replaced_frames": 1,
+    "right_anchor": 2019,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 12.0,
+    "rotation_residual_p95_deg": 19.761418734705604,
+    "translation_inlier_threshold_mm": 20.0,
+    "translation_residual_p95_mm": 7.840437555723797
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_014.parquet::left:2047-2047:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 68.233–68.233s

view_middleview_leftview_right

x (mm)21.27-14.49y (mm)-159.16-275.73z (mm)120.2343.80step (deg)161.400.0067.23s69.23s
{
+  "source_start_row": 2047,
+  "source_end_row": 2047,
+  "local_start_row": 2047,
+  "local_end_row": 2047,
+  "capture_start_seconds": 69.07355093955994,
+  "capture_end_seconds": 69.07355093955994,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 161.39728070711539,
+    "anchor_translation_mm": 102.24339167842989,
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+    "duration_frames": 1,
+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 2046,
+    "left_context_frames": 15,
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+    "physical": false,
+    "prediction_rotation_max_deg": 80.69864035355769,
+    "prediction_translation_max_mm": 51.121695839214944,
+    "reconstructed_max_step_rotation_deg": 80.69864035355766,
+    "reconstructed_max_step_translation_mm": 51.121695839214944,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 2048,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 80.69864035355769,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 51.121695839214944
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_014.parquet::left:2091-2091:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 69.700–69.700s

view_middleview_leftview_right

x (mm)114.6514.45y (mm)-150.97-159.46z (mm)120.2358.91step (deg)168.200.0068.70s70.70s
{
+  "source_start_row": 2091,
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+  "local_start_row": 2091,
+  "local_end_row": 2091,
+  "capture_start_seconds": 70.54836177825928,
+  "capture_end_seconds": 70.54836177825928,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 168.19500564948964,
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+    "duration_frames": 1,
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+    "fit_iterations": 0,
+    "left_anchor": 2090,
+    "left_context_frames": 15,
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+    "physical": false,
+    "prediction_rotation_max_deg": 84.09750282474482,
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+    "reconstructed_max_step_rotation_deg": 84.09750282474481,
+    "reconstructed_max_step_translation_mm": 29.08706778890509,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 2092,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 84.09750282474484,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 29.08706778890509
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_014.parquet::left:458-492:known_gap — LOW — unrepaired_ambiguous_branch — 15.267–16.400s

view_middleview_leftview_right

x (mm)244.01-47.75y (mm)-386.81-467.20z (mm)102.4558.28step (deg)97.920.2314.27s17.40s
{
+  "source_start_row": 458,
+  "source_end_row": 492,
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+  "local_end_row": 492,
+  "capture_start_seconds": 15.420008897781372,
+  "capture_end_seconds": 16.56186056137085,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "known_gap",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 35,
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+    "fit_iterations": 2,
+    "left_anchor": 457,
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+    "prediction_rotation_max_deg": 31.30478917987842,
+    "prediction_translation_max_mm": 274.3213416529898,
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+    "reconstructed_max_step_translation_mm": 6.468371201509649,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 35,
+    "right_anchor": 493,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 12.0,
+    "rotation_residual_p95_deg": 76.61991311616964,
+    "translation_inlier_threshold_mm": 12.787110173635421,
+    "translation_residual_p95_mm": 84.27302741577937
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_014.parquet::left:665-665:branch_discontinuity — MEDIUM — endpoint_se3 — 22.167–22.167s

view_middleview_leftview_right

x (mm)65.221.43y (mm)-430.16-467.99z (mm)119.7864.93step (deg)12.720.0021.17s23.17s
{
+  "source_start_row": 665,
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+  "local_start_row": 665,
+  "local_end_row": 665,
+  "capture_start_seconds": 22.37036108970642,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 36.923571228112,
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+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 664,
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+    "prediction_rotation_max_deg": 6.3607366994458925,
+    "prediction_translation_max_mm": 18.461785614056,
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+    "replaced_frames": 1,
+    "right_anchor": 666,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 12.0,
+    "rotation_residual_p95_deg": 6.3607366994458925,
+    "translation_inlier_threshold_mm": 12.701457933025189,
+    "translation_residual_p95_mm": 18.461785614056
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_014.parquet::left:916-916:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 30.533–30.533s

view_middleview_leftview_right

x (mm)17.84-26.08y (mm)-190.43-454.62z (mm)66.9747.77step (deg)63.290.0029.53s31.53s
{
+  "source_start_row": 916,
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+  "local_end_row": 916,
+  "capture_start_seconds": 30.837235689163208,
+  "capture_end_seconds": 30.837235689163208,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "duration_frames": 1,
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+    "fit_iterations": 0,
+    "left_anchor": 915,
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+    "prediction_rotation_max_deg": 34.85541493070357,
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+    "reconstructed_max_step_rotation_deg": 34.85541493070357,
+    "reconstructed_max_step_translation_mm": 8.38600078891969,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 917,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 12.0,
+    "rotation_residual_p95_deg": 28.43091005635518,
+    "translation_inlier_threshold_mm": 19.31983616158974,
+    "translation_residual_p95_mm": 9.42732709839082
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_014.parquet::left:933-933:branch_discontinuity — MEDIUM — endpoint_se3 — 31.100–31.100s

view_middleview_leftview_right

x (mm)13.01-26.08y (mm)-151.64-379.83z (mm)68.0647.77step (deg)111.260.0030.10s32.10s
{
+  "source_start_row": 933,
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+  "local_start_row": 933,
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+  "capture_start_seconds": 31.41136336326599,
+  "capture_end_seconds": 31.41136336326599,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 51.59721119177633,
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+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 932,
+    "left_context_frames": 15,
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+    "prediction_rotation_max_deg": 19.923061115417816,
+    "prediction_translation_max_mm": 25.798605595888166,
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+    "reconstructed_max_step_translation_mm": 25.798605595888166,
+    "replaced_frames": 1,
+    "right_anchor": 934,
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+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 19.923061115417852,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 25.798605595888166
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_014.parquet::left:963-963:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 32.100–32.100s

view_middleview_leftview_right

x (mm)129.04-26.08y (mm)-146.00-213.92z (mm)69.7156.58step (deg)111.260.0031.10s33.10s
{
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+  "capture_start_seconds": 32.41594052314758,
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+  "kind": "branch_discontinuity",
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+    "fit_iterations": 0,
+    "left_anchor": 962,
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+    "reconstructed_max_step_translation_mm": 27.567070062492878,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 964,
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+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 55.63106395406927,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 27.567070062492878
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_014.parquet::right:1706-1706:branch_discontinuity — MEDIUM — endpoint_se3 — 56.867–56.867s

view_middleview_leftview_right

x (mm)843.59774.79y (mm)-365.99-515.04z (mm)294.7172.78step (deg)19.700.0055.87s57.87s
{
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+  "local_end_row": 1706,
+  "capture_start_seconds": 57.4468560218811,
+  "capture_end_seconds": 57.4468560218811,
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+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 67.6407241435301,
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+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "independent_loss_evidence": false,
+    "left_anchor": 1705,
+    "left_context_frames": 15,
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+    "physical": true,
+    "prediction_rotation_max_deg": 9.848538977867566,
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+    "reconstructed_max_step_translation_mm": 33.82036207176505,
+    "replaced_frames": 1,
+    "review_required": true,
+    "right_anchor": 1707,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 9.848538977867632,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 33.82036207176505
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::left:2144-2170:known_gap — LOW — unrepaired_ambiguous_branch — 71.467–72.333s

view_middleview_leftview_right

x (mm)33.72-11.76y (mm)-103.58-357.82z (mm)137.8247.87step (deg)103.090.0070.47s73.33s
{
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+  "local_end_row": 2170,
+  "capture_start_seconds": 73.20453262329102,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "known_gap",
+  "evidence": {
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+    "duration_frames": 27,
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+    "fit_iterations": 2,
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+    "prediction_rotation_max_deg": 23.24384180675123,
+    "prediction_translation_max_mm": 121.6324153454414,
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+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 27,
+    "right_anchor": 2171,
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+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 103.05412134504903,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 113.11155828169609
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::left:2214-2214:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 73.800–73.800s

view_middleview_leftview_right

x (mm)49.51-12.09y (mm)-103.58-203.73z (mm)138.7366.70step (deg)174.630.0072.80s74.80s
{
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+  "capture_start_seconds": 75.59486722946167,
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+  "evidence": {
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+    "duration_frames": 1,
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+    "fit_iterations": 0,
+    "left_anchor": 2213,
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+    "prediction_rotation_max_deg": 87.31460644845437,
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+    "reconstructed_max_step_translation_mm": 43.8146719391689,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 2215,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 87.31460644845436,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 43.8146719391689
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::left:2240-2240:branch_discontinuity — MEDIUM — endpoint_se3 — 74.667–74.667s

view_middleview_leftview_right

x (mm)78.09-12.09y (mm)-151.11-203.73z (mm)138.7366.70step (deg)174.630.0073.67s75.67s
{
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+  "local_end_row": 2240,
+  "capture_start_seconds": 76.46440958976746,
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+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 23.862553176254156,
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+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 2239,
+    "left_context_frames": 15,
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+    "prediction_rotation_max_deg": 21.909860518847797,
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+    "reconstructed_max_step_translation_mm": 11.931276588127078,
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+    "right_anchor": 2241,
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+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 21.909860518847783,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 11.931276588127078
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::left:2264-2264:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 75.467–75.467s

view_middleview_leftview_right

x (mm)78.0935.31y (mm)-159.71-203.73z (mm)99.9866.70step (deg)150.480.0074.47s76.47s
{
+  "source_start_row": 2264,
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+  "local_end_row": 2264,
+  "capture_start_seconds": 77.27097058296204,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "duration_frames": 1,
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+    "fit_iterations": 0,
+    "left_anchor": 2263,
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+    "prediction_rotation_max_deg": 75.23827316161083,
+    "prediction_translation_max_mm": 30.817344600944125,
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+    "reconstructed_max_step_translation_mm": 30.817344600944125,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 2265,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 75.23827316161082,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 30.817344600944125
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::left:2301-2301:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 76.700–76.700s

view_middleview_leftview_right

x (mm)100.8811.12y (mm)-140.48-159.71z (mm)99.9859.62step (deg)121.030.0075.70s77.70s
{
+  "source_start_row": 2301,
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+  "local_end_row": 2301,
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+  "capture_end_seconds": 78.50967693328857,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 121.03358683131665,
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+    "duration_frames": 1,
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+    "left_anchor": 2300,
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+    "prediction_rotation_max_deg": 60.51679341565833,
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+    "reconstructed_max_step_rotation_deg": 60.51679341565835,
+    "reconstructed_max_step_translation_mm": 40.1818058251834,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 2302,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 60.51679341565833,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 40.1818058251834
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::left:3588-3588:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 119.600–119.600s

view_middleview_leftview_right

x (mm)176.4618.22y (mm)-328.30-424.23z (mm)139.4570.95step (deg)167.240.00118.60s120.60s
{
+  "source_start_row": 3588,
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+  "local_end_row": 3588,
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+  "capture_end_seconds": 122.08715009689331,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
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+    "fit_iterations": 0,
+    "left_anchor": 3587,
+    "left_context_frames": 15,
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+    "prediction_rotation_max_deg": 52.76796720723103,
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+    "reconstructed_max_step_translation_mm": 23.700502880405494,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 3589,
+    "right_context_frames": 15,
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+    "rotation_residual_p95_deg": 49.76417151785797,
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+    "translation_residual_p95_mm": 16.28658105739279
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::left:3602-3602:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 120.067–120.067s

view_middleview_leftview_right

x (mm)176.4614.97y (mm)-227.68-424.23z (mm)136.6970.95step (deg)167.240.00119.07s121.07s
{
+  "source_start_row": 3602,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "duration_frames": 1,
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+    "fit_iterations": 0,
+    "left_anchor": 3601,
+    "left_context_frames": 15,
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+    "prediction_rotation_max_deg": 83.61945848055531,
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+    "reconstructed_max_step_rotation_deg": 83.61945848055532,
+    "reconstructed_max_step_translation_mm": 49.1334686350823,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 3603,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 83.61945848055532,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 49.1334686350823
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::left:3623-3623:branch_discontinuity — MEDIUM — endpoint_se3 — 120.767–120.767s

view_middleview_leftview_right

x (mm)39.1214.97y (mm)-227.68-424.23z (mm)117.01111.57step (deg)167.240.00119.77s121.77s
{
+  "source_start_row": 3623,
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+  "local_end_row": 3623,
+  "capture_start_seconds": 123.26340746879578,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "fit_iterations": 0,
+    "independent_loss_evidence": false,
+    "left_anchor": 3622,
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+    "prediction_rotation_max_deg": 6.778335000993984,
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+    "replaced_frames": 1,
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+    "right_anchor": 3624,
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+    "rotation_residual_p95_deg": 6.778335000993972,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 47.136341734285445
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::left:3669-3669:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 122.300–122.300s

view_middleview_leftview_right

x (mm)28.8014.97y (mm)-176.66-227.68z (mm)111.5785.58step (deg)176.940.00121.30s123.30s
{
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "duration_frames": 1,
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+    "fit_iterations": 0,
+    "left_anchor": 3668,
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+    "prediction_rotation_max_deg": 88.46778194005338,
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+    "reconstructed_max_step_translation_mm": 29.457158504663738,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 3670,
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+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 29.457158504663738
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::left:4799-4822:known_gap — LOW — unrepaired_ambiguous_branch — 159.967–160.733s

view_middleview_leftview_right

x (mm)27.63-32.62y (mm)-150.55-431.25z (mm)65.8437.67step (deg)173.870.00158.97s161.73s
{
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+  "kind": "known_gap",
+  "evidence": {
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+    "duration_frames": 24,
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+    "fit_iterations": 2,
+    "left_anchor": 4798,
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+    "physical": true,
+    "prediction_rotation_max_deg": 77.13821300995028,
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+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 24,
+    "right_anchor": 4823,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 12.0,
+    "rotation_residual_p95_deg": 94.92484171780369,
+    "translation_inlier_threshold_mm": 13.218950188011798,
+    "translation_residual_p95_mm": 47.5162843068848
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::left:4837-4837:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 161.233–161.233s

view_middleview_leftview_right

x (mm)28.50-25.07y (mm)-136.52-333.29z (mm)87.1737.67step (deg)173.870.00160.23s162.23s
{
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "duration_frames": 1,
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+    "fit_iterations": 0,
+    "left_anchor": 4836,
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+    "reconstructed_max_step_translation_mm": 50.26938561790592,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 4838,
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+    "rotation_residual_p95_deg": 86.93255616917253,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 50.26938561790592
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::left:4887-4887:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 162.900–162.900s

view_middleview_leftview_right

x (mm)160.6915.71y (mm)-129.92-158.57z (mm)87.1743.05step (deg)178.930.00161.90s163.90s
{
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+  "capture_start_seconds": 165.96845507621765,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "fit_iterations": 0,
+    "left_anchor": 4886,
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+    "prediction_rotation_max_deg": 89.46605467879363,
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+    "reconstructed_max_step_translation_mm": 22.53719938968451,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 4888,
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+    "rotation_residual_p95_deg": 89.46605467879363,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 22.53719938968451
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::left:708-768:known_gap — LOW — unrepaired_ambiguous_branch — 23.600–25.600s

view_middleview_leftview_right

x (mm)197.4322.84y (mm)-151.43-405.39z (mm)79.8838.40step (deg)121.060.0522.60s26.60s
{
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+  "kind": "known_gap",
+  "evidence": {
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+    "duration_frames": 61,
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+    "fit_iterations": 2,
+    "left_anchor": 707,
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+    "prediction_rotation_max_deg": 32.35704374926785,
+    "prediction_translation_max_mm": 338.7908765764906,
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+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 61,
+    "right_anchor": 769,
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+    "rotation_residual_p95_deg": 123.60127502810681,
+    "translation_inlier_threshold_mm": 6.682498439356882,
+    "translation_residual_p95_mm": 92.30468278281418
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::right:1503-1503:branch_discontinuity — MEDIUM — endpoint_se3 — 50.100–50.100s

view_middleview_leftview_right

x (mm)842.65775.26y (mm)-391.25-538.17z (mm)246.8665.41step (deg)12.500.0049.10s51.10s
{
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+  "local_end_row": 1503,
+  "capture_start_seconds": 50.793739795684814,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 23.872960975015637,
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+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "independent_loss_evidence": false,
+    "left_anchor": 1502,
+    "left_context_frames": 15,
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+    "prediction_rotation_max_deg": 6.248012738553094,
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+    "reconstructed_max_step_rotation_deg": 6.248012738552869,
+    "reconstructed_max_step_translation_mm": 11.936480487507819,
+    "replaced_frames": 1,
+    "review_required": true,
+    "right_anchor": 1504,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 6.248012738553171,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 11.936480487507819
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::right:4164-4164:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 138.800–138.800s

view_middleview_leftview_right

x (mm)822.27723.41y (mm)-268.68-525.55z (mm)368.42114.39step (deg)12.330.00137.80s139.80s
{
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+  "capture_start_seconds": 141.64207482337952,
+  "capture_end_seconds": 141.64207482337952,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 105.35050731160781,
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+    "duration_frames": 1,
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+    "fit_iterations": 0,
+    "left_anchor": 4163,
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+    "prediction_rotation_max_deg": 6.165415767131308,
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+    "reconstructed_max_step_translation_mm": 52.67525365580391,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 4165,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 6.165415767131426,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 52.67525365580391
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::right:5380-5395:known_gap — LOW — unrepaired_ambiguous_branch — 179.333–179.833s

view_middleview_leftview_right

x (mm)833.34664.20y (mm)-225.59-496.71z (mm)312.8731.52step (deg)152.340.00178.33s180.83s
{
+  "source_start_row": 5380,
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+  "local_end_row": 5395,
+  "capture_start_seconds": 182.63725399971008,
+  "capture_end_seconds": 183.1382942199707,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "known_gap",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 16,
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+    "fit_iterations": 2,
+    "left_anchor": 5379,
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+    "physical": true,
+    "prediction_rotation_max_deg": 18.095197991820076,
+    "prediction_translation_max_mm": 234.68641159827368,
+    "reconstructed_max_step_rotation_deg": 0.9769791054911215,
+    "reconstructed_max_step_translation_mm": 14.927520664969382,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 16,
+    "right_anchor": 5396,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 9.647743289470347,
+    "rotation_residual_p95_deg": 151.75909107130786,
+    "translation_inlier_threshold_mm": 20.0,
+    "translation_residual_p95_mm": 413.4206346901394
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_016.parquet::left:1087-1134:gap_with_discontinuity — LOW — unrepaired_ambiguous_branch — 36.233–37.800s

view_middleview_leftview_right

x (mm)-17.78-45.16y (mm)-244.05-367.26z (mm)71.1232.06step (deg)75.970.0035.23s38.80s
{
+  "source_start_row": 1087,
+  "source_end_row": 1134,
+  "local_start_row": 1087,
+  "local_end_row": 1134,
+  "capture_start_seconds": 37.51837158203125,
+  "capture_end_seconds": 39.25580310821533,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "gap_with_discontinuity",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 48,
+    "fit_converged": true,
+    "fit_iterations": 2,
+    "left_anchor": 1086,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": true,
+    "prediction_rotation_max_deg": 65.11376851495464,
+    "prediction_translation_max_mm": 108.9624191056293,
+    "reconstructed_max_step_rotation_deg": 1.9929453757721174,
+    "reconstructed_max_step_translation_mm": 1.8635740260761091,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 48,
+    "right_anchor": 1135,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 72.7688770670244,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 54.649506452420866
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_016.parquet::left:1152-1152:branch_discontinuity — MEDIUM — endpoint_se3 — 38.400–38.400s

view_middleview_leftview_right

x (mm)-23.74-43.83y (mm)-251.46-398.95z (mm)71.1232.06step (deg)75.970.0037.40s39.40s
{
+  "source_start_row": 1152,
+  "source_end_row": 1152,
+  "local_start_row": 1152,
+  "local_end_row": 1152,
+  "capture_start_seconds": 39.98921751976013,
+  "capture_end_seconds": 39.98921751976013,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 13.236548624644133,
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+    "branch_unambiguous": true,
+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "independent_loss_evidence": false,
+    "left_anchor": 1151,
+    "left_context_frames": 15,
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+    "physical": true,
+    "prediction_rotation_max_deg": 6.618274312322066,
+    "prediction_translation_max_mm": 21.831409142176035,
+    "reconstructed_max_step_rotation_deg": 6.618274312321834,
+    "reconstructed_max_step_translation_mm": 21.831409142176035,
+    "replaced_frames": 1,
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+    "right_anchor": 1153,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 11.414627660999821,
+    "rotation_residual_p95_deg": 6.618274312322134,
+    "translation_inlier_threshold_mm": 5.449299658716763,
+    "translation_residual_p95_mm": 21.831409142176035
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_016.parquet::left:2448-2597:known_gap — LOW — unrepaired_ambiguous_branch — 81.600–86.567s

view_middleview_leftview_right

x (mm)70.11-24.14y (mm)-235.66-496.95z (mm)116.5237.08step (deg)172.430.0080.60s87.57s
{
+  "source_start_row": 2448,
+  "source_end_row": 2597,
+  "local_start_row": 2448,
+  "local_end_row": 2597,
+  "capture_start_seconds": 84.73347353935242,
+  "capture_end_seconds": 91.43775224685669,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "known_gap",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 150,
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+    "fit_iterations": 2,
+    "left_anchor": 2447,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": true,
+    "prediction_rotation_max_deg": 121.71907859540475,
+    "prediction_translation_max_mm": 409.7130149130925,
+    "reconstructed_max_step_rotation_deg": 0.7904680143078135,
+    "reconstructed_max_step_translation_mm": 3.2956659691634163,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 150,
+    "right_anchor": 2598,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 12.0,
+    "rotation_residual_p95_deg": 110.88162619979325,
+    "translation_inlier_threshold_mm": 17.008321972108202,
+    "translation_residual_p95_mm": 128.8840009906193
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_016.parquet::left:3731-3734:branch_discontinuity — MEDIUM — endpoint_se3 — 124.367–124.467s

view_middleview_leftview_right

x (mm)80.29-7.78y (mm)-329.51-544.91z (mm)221.4261.08step (deg)39.830.00123.37s125.47s
{
+  "source_start_row": 3731,
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+  "local_end_row": 3734,
+  "capture_start_seconds": 130.8509705066681,
+  "capture_end_seconds": 130.9508445262909,
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+  "kind": "branch_discontinuity",
+  "evidence": {
+    "anchor_rotation_deg": 25.159817494114108,
+    "anchor_translation_mm": 140.9597493976658,
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+    "duration_frames": 4,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 3730,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
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+    "prediction_rotation_max_deg": 5.0319634988228215,
+    "prediction_translation_max_mm": 28.191949879533162,
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+    "reconstructed_max_step_translation_mm": 28.191949879533187,
+    "replaced_frames": 4,
+    "right_anchor": 3735,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 5.413304363592505,
+    "rotation_residual_p95_deg": 35.40268252065805,
+    "translation_inlier_threshold_mm": 10.468114595026139,
+    "translation_residual_p95_mm": 82.14103248105783
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_016.parquet::left:4936-5002:known_gap — LOW — unrepaired_ambiguous_branch — 164.533–166.733s

view_middleview_leftview_right

x (mm)26.82-43.05y (mm)-211.69-328.17z (mm)92.5240.63step (deg)115.490.00163.53s167.73s
{
+  "source_start_row": 4936,
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+  "local_end_row": 5002,
+  "capture_start_seconds": 172.0431203842163,
+  "capture_end_seconds": 174.3805763721466,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "known_gap",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 67,
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+    "fit_iterations": 2,
+    "left_anchor": 4935,
+    "left_context_frames": 15,
+    "observed_inlier_frames": 0,
+    "physical": true,
+    "prediction_rotation_max_deg": 40.5809842944561,
+    "prediction_translation_max_mm": 5.5619465775679635e-14,
+    "reconstructed_max_step_rotation_deg": 0.4513559088786942,
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+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 67,
+    "right_anchor": 5003,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 124.01904633043839,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 42.873797666955845
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_016.parquet::left:5103-5150:known_gap — LOW — unrepaired_ambiguous_branch — 170.100–171.667s

view_middleview_leftview_right

x (mm)126.17-32.19y (mm)-280.70-461.83z (mm)121.2958.84step (deg)45.870.00169.10s172.67s
{
+  "source_start_row": 5103,
+  "source_end_row": 5150,
+  "local_start_row": 5103,
+  "local_end_row": 5150,
+  "capture_start_seconds": 177.87084698677063,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "known_gap",
+  "evidence": {
+    "branch_unambiguous": true,
+    "duration_frames": 48,
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+    "fit_iterations": 2,
+    "left_anchor": 5102,
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+    "prediction_rotation_max_deg": 28.12198589231144,
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+    "reconstructed_max_step_translation_mm": 10.63378171133979,
+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 48,
+    "right_anchor": 5151,
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+    "rotation_inlier_threshold_deg": 7.39133721361034,
+    "rotation_residual_p95_deg": 57.85264319914962,
+    "translation_inlier_threshold_mm": 13.57456062530042,
+    "translation_residual_p95_mm": 93.64346836657583
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_016.parquet::left:927-927:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 30.900–30.900s

view_middleview_leftview_right

x (mm)68.16-52.42y (mm)-200.30-234.06z (mm)79.9939.53step (deg)100.880.0029.90s31.90s
{
+  "source_start_row": 927,
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+  "capture_start_seconds": 31.72392249107361,
+  "capture_end_seconds": 31.72392249107361,
+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 33.65643444315345,
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+    "duration_frames": 1,
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+    "fit_iterations": 0,
+    "left_anchor": 926,
+    "left_context_frames": 15,
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+    "physical": false,
+    "prediction_rotation_max_deg": 47.85188340302068,
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+    "reconstructed_max_step_rotation_deg": 47.85188340302068,
+    "reconstructed_max_step_translation_mm": 16.828217221576725,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 928,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 4.563120905977927,
+    "rotation_residual_p95_deg": 53.03097306992733,
+    "translation_inlier_threshold_mm": 16.36098598276215,
+    "translation_residual_p95_mm": 16.986489363226404
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_016.parquet::left:981-981:branch_discontinuity — MEDIUM — endpoint_se3 — 32.700–32.700s

view_middleview_leftview_right

x (mm)-29.23-52.42y (mm)-218.35-260.12z (mm)54.6146.12step (deg)24.580.0031.70s33.70s
{
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+  "local_start_row": 981,
+  "local_end_row": 981,
+  "capture_start_seconds": 33.639960527420044,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 48.52297068880732,
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+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 980,
+    "left_context_frames": 15,
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+    "prediction_rotation_max_deg": 12.291325231917234,
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+    "reconstructed_max_step_translation_mm": 24.26148534440366,
+    "replaced_frames": 1,
+    "right_anchor": 982,
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+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 12.291325231917243,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 24.26148534440366
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_016.parquet::right:4264-4264:branch_discontinuity — MEDIUM — endpoint_se3 — 142.133–142.133s

view_middleview_leftview_right

x (mm)659.09415.54y (mm)-352.69-554.97z (mm)367.7158.54step (deg)19.030.00141.13s143.13s
{
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+  "local_end_row": 4264,
+  "capture_start_seconds": 149.0593912601471,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "independent_loss_evidence": false,
+    "left_anchor": 4263,
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+    "prediction_rotation_max_deg": 9.514635376621122,
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+    "replaced_frames": 1,
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+    "right_anchor": 4265,
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+    "rotation_inlier_threshold_deg": 6.26567587190447,
+    "rotation_residual_p95_deg": 9.514635376621182,
+    "translation_inlier_threshold_mm": 20.0,
+    "translation_residual_p95_mm": 23.79407674720982
+  }
+}
old_data/motherboard/meta/2026-05-11/episode_017.parquet::right:2561-2593:known_gap — MEDIUM — robust_se3_hermite — 85.367–86.433s

view_middleview_leftview_right

x (mm)520.22477.20y (mm)-205.36-233.49z (mm)70.2862.42step (deg)19.730.0584.37s87.43s
{
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+  "capture_start_seconds": 87.41854667663574,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "known_gap",
+  "evidence": {
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+    "duration_frames": 33,
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+    "fit_iterations": 2,
+    "left_anchor": 2560,
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+    "prediction_rotation_max_deg": 2.8769662653795742,
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+    "right_anchor": 2594,
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+    "rotation_residual_p95_deg": 17.072807018762088,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 5.410490475164184
+  }
+}
old_data/motherboard/meta/2026-05-19/episode_000.parquet::left:1353-1353:branch_discontinuity — MEDIUM — endpoint_se3 — 45.100–45.100s

view_middleview_leftview_right

x (mm)336.52261.29y (mm)-294.25-386.21z (mm)102.4571.22step (deg)13.020.1544.10s46.10s
{
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+  "capture_start_seconds": 128.65130949020386,
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+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "anchor_translation_mm": 36.773009145923815,
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+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 1352,
+    "left_context_frames": 15,
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+    "prediction_rotation_max_deg": 8.987116045250412,
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+    "rotation_inlier_threshold_deg": 7.730971947692646,
+    "rotation_residual_p95_deg": 4.400681468756097,
+    "translation_inlier_threshold_mm": 17.711927418215396,
+    "translation_residual_p95_mm": 6.770352880520333
+  }
+}
old_data/motherboard/meta/2026-05-19/episode_000.parquet::left:51-52:branch_discontinuity — MEDIUM — endpoint_se3 — 1.700–1.733s

view_middleview_leftview_right

x (mm)402.31206.40y (mm)-309.07-358.06z (mm)130.8873.79step (deg)18.420.030.70s2.73s
{
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+    "duration_frames": 2,
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+    "fit_iterations": 0,
+    "left_anchor": 50,
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+    "prediction_rotation_max_deg": 18.42073900998481,
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+    "replaced_frames": 0,
+    "right_anchor": 53,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 9.486243854901828,
+    "rotation_residual_p95_deg": 2.1836354397233776e-14,
+    "translation_inlier_threshold_mm": 20.0,
+    "translation_residual_p95_mm": 0.0
+  }
+}
old_data/motherboard/meta/2026-05-19/episode_001.parquet::left:49-49:branch_discontinuity — MEDIUM — endpoint_se3 — 1.633–1.633s

view_middleview_leftview_right

x (mm)384.69194.29y (mm)-216.40-351.66z (mm)133.7888.35step (deg)12.760.020.63s2.63s
{
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+  "local_start_row": 49,
+  "local_end_row": 49,
+  "capture_start_seconds": 4.620274305343628,
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+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 48,
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+    "prediction_rotation_max_deg": 11.594624757658421,
+    "prediction_translation_max_mm": 30.165093135703934,
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+    "reconstructed_max_step_translation_mm": 37.11901306634927,
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+    "right_anchor": 50,
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+    "rotation_inlier_threshold_deg": 6.41401759855603,
+    "rotation_residual_p95_deg": 2.5457499486410162,
+    "translation_inlier_threshold_mm": 17.41049643471215,
+    "translation_residual_p95_mm": 11.834157173856019
+  }
+}
old_data/motherboard/meta/2026-05-19/episode_002.parquet::left:1490-1490:branch_discontinuity — MEDIUM — endpoint_se3 — 49.667–49.667s

view_middleview_leftview_right

x (mm)427.55275.58y (mm)-214.49-347.84z (mm)144.4478.03step (deg)36.180.0648.67s50.67s
{
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+  "capture_start_seconds": 133.45634126663208,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "duration_frames": 1,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 1489,
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+    "prediction_rotation_max_deg": 18.766102926122198,
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+    "replaced_frames": 1,
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+}
old_data/motherboard/meta/2026-05-19/episode_002.parquet::left:873-873:branch_discontinuity — MEDIUM — endpoint_se3 — 29.100–29.100s

view_middleview_leftview_right

x (mm)341.92159.68y (mm)-229.74-455.68z (mm)240.5885.52step (deg)16.790.2528.10s30.10s
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+}
old_data/motherboard/meta/2026-05-19/episode_002.parquet::left:9-9:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 0.300–0.300s

view_middleview_leftview_right

x (mm)363.45205.36y (mm)-309.59-366.67z (mm)150.65108.72step (deg)23.430.150.00s1.30s
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+}
old_data/motherboard/meta/2026-05-19/episode_002.parquet::right:0-0:branch_discontinuity — LOW — unrepaired_missing_anchor — 0.000–0.000s

view_middleview_leftview_right

x (mm)634.13537.38y (mm)-344.02-415.16z (mm)142.0786.17step (deg)146.240.110.00s1.00s
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+}
old_data/motherboard/meta/2026-05-19/episode_002.parquet::right:130-130:branch_discontinuity — MEDIUM — endpoint_se3 — 4.333–4.333s

view_middleview_leftview_right

x (mm)490.40377.73y (mm)-360.36-476.19z (mm)151.2776.46step (deg)12.180.183.33s5.33s
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+}
old_data/motherboard/meta/2026-05-19/episode_002.parquet::right:19-19:branch_discontinuity — MEDIUM — endpoint_se3 — 0.633–0.633s

view_middleview_leftview_right

x (mm)634.13459.64y (mm)-344.02-433.11z (mm)142.0779.74step (deg)146.240.100.00s1.63s
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+}
old_data/pushT/meta/2026-06-18/episode_000.parquet::right:1851-1851:branch_discontinuity — MEDIUM — endpoint_se3 — 61.700–61.700s

view_middleview_leftview_right

x (mm)354.47171.53y (mm)-327.16-374.09z (mm)247.84195.06step (deg)12.450.2860.70s62.70s
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+}
old_data/pushT/meta/2026-06-18/episode_000.parquet::right:1975-1975:branch_discontinuity — MEDIUM — endpoint_se3 — 65.833–65.833s

view_middleview_leftview_right

x (mm)505.99393.47y (mm)-277.44-361.51z (mm)118.3584.99step (deg)15.840.0964.83s66.83s
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+}
old_data/pushT/meta/2026-06-18/episode_001.parquet::left:11024-11027:branch_discontinuity — MEDIUM — endpoint_se3 — 367.467–367.567s

view_middleview_leftview_right

x (mm)353.21181.21y (mm)-109.27-185.03z (mm)115.1136.31step (deg)140.520.35366.47s368.57s
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+}
old_data/pushT/meta/2026-06-18/episode_001.parquet::left:11247-11247:branch_discontinuity — MEDIUM — endpoint_se3 — 374.900–374.900s

view_middleview_leftview_right

x (mm)312.97142.69y (mm)-225.14-432.65z (mm)326.3894.45step (deg)37.760.00373.90s375.90s
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+}
old_data/pushT/meta/2026-06-18/episode_001.parquet::left:11267-11267:branch_discontinuity — MEDIUM — endpoint_se3 — 375.567–375.567s

view_middleview_leftview_right

x (mm)217.97142.69y (mm)-294.81-432.65z (mm)326.38234.43step (deg)37.760.00374.57s376.57s
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+}
old_data/pushT/meta/2026-06-18/episode_001.parquet::left:13954-13954:branch_review — MEDIUM — paired_body_branch_review — 465.133–465.133s

view_middleview_leftview_right

x (mm)254.55182.93y (mm)-231.21-265.04z (mm)184.6987.89step (deg)36.810.13464.13s466.13s
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+}
old_data/pushT/meta/2026-06-18/episode_001.parquet::left:13974-13977:branch_review — MEDIUM — paired_body_branch_review — 465.800–465.900s

view_middleview_leftview_right

x (mm)205.74182.93y (mm)-231.21-244.91z (mm)118.9985.92step (deg)36.810.13464.80s466.90s
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old_data/pushT/meta/2026-06-18/episode_001.parquet::left:4166-4166:branch_review — MEDIUM — paired_body_branch_review — 138.867–138.867s

view_middleview_leftview_right

x (mm)198.13173.20y (mm)-208.84-349.26z (mm)253.7390.35step (deg)40.680.20137.87s139.87s
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old_data/pushT/meta/2026-06-18/episode_001.parquet::left:4177-4178:branch_review — MEDIUM — paired_body_branch_review — 139.233–139.267s

view_middleview_leftview_right

x (mm)198.13173.20y (mm)-227.37-349.26z (mm)253.73144.84step (deg)40.680.14138.23s140.27s
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old_data/pushT/meta/2026-06-18/episode_001.parquet::left:7981-7984:branch_discontinuity — MEDIUM — endpoint_se3 — 266.033–266.133s

view_middleview_leftview_right

x (mm)313.17203.08y (mm)-119.25-224.15z (mm)137.2757.95step (deg)144.550.40265.03s267.13s
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old_data/pushT/meta/2026-06-18/episode_001.parquet::left:9752-9758:branch_review — MEDIUM — paired_body_branch_review — 325.067–325.267s

view_middleview_leftview_right

x (mm)297.78144.57y (mm)-201.97-222.31z (mm)134.1195.68step (deg)36.240.08324.07s326.27s
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old_data/pushT/meta/2026-06-18/episode_001.parquet::right:12800-12800:branch_discontinuity — MEDIUM — endpoint_se3 — 426.667–426.667s

view_middleview_leftview_right

x (mm)617.78429.44y (mm)-145.98-286.32z (mm)130.4985.42step (deg)12.180.17425.67s427.67s
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old_data/pushT/meta/2026-06-18/episode_001.parquet::right:13599-13604:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 453.300–453.467s

view_middleview_leftview_right

x (mm)466.09332.93y (mm)-245.64-320.66z (mm)229.2791.59step (deg)45.330.00452.30s454.47s
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old_data/pushT/meta/2026-06-18/episode_001.parquet::right:13785-13785:branch_review — MEDIUM — paired_body_branch_review — 459.500–459.500s

view_middleview_leftview_right

x (mm)516.97495.07y (mm)-206.20-255.29z (mm)103.1469.59step (deg)166.460.13458.50s460.50s
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old_data/pushT/meta/2026-06-18/episode_001.parquet::right:13819-13820:branch_review — MEDIUM — paired_body_branch_review — 460.633–460.667s

view_middleview_leftview_right

x (mm)527.24475.70y (mm)-220.53-259.64z (mm)193.8169.59step (deg)158.380.00459.63s461.67s
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old_data/pushT/meta/2026-06-18/episode_001.parquet::right:454-454:branch_discontinuity — MEDIUM — endpoint_se3 — 15.133–15.133s

view_middleview_leftview_right

x (mm)532.45345.33y (mm)-147.30-277.13z (mm)134.1865.88step (deg)14.480.4314.13s16.13s
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+}
old_data/pushT/meta/2026-06-18/episode_001.parquet::right:4800-4800:branch_review — MEDIUM — paired_body_branch_review — 160.000–160.000s

view_middleview_leftview_right

x (mm)436.85346.83y (mm)-206.61-248.60z (mm)170.8674.00step (deg)157.770.00159.00s161.00s
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old_data/pushT/meta/2026-06-18/episode_002.parquet::left:1130-1130:branch_discontinuity — MEDIUM — endpoint_se3 — 37.667–37.667s

view_middleview_leftview_right

x (mm)344.04162.67y (mm)-54.61-251.65z (mm)202.2893.46step (deg)13.230.0636.67s38.67s
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old_data/pushT/meta/2026-06-18/episode_002.parquet::left:1171-1171:branch_discontinuity — MEDIUM — endpoint_se3 — 39.033–39.033s

view_middleview_leftview_right

x (mm)370.19267.87y (mm)-89.08-212.93z (mm)131.6593.46step (deg)15.220.3738.03s40.03s
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old_data/pushT/meta/2026-06-18/episode_002.parquet::left:1201-1201:branch_discontinuity — MEDIUM — endpoint_se3 — 40.033–40.033s

view_middleview_leftview_right

x (mm)396.47277.81y (mm)-91.34-307.77z (mm)117.7698.74step (deg)15.220.3739.03s41.03s
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old_data/pushT/meta/2026-06-18/episode_002.parquet::left:1240-1240:branch_discontinuity — MEDIUM — endpoint_se3 — 41.333–41.333s

view_middleview_leftview_right

x (mm)397.42268.28y (mm)-141.58-315.54z (mm)121.9488.87step (deg)20.290.3140.33s42.33s
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old_data/pushT/meta/2026-06-18/episode_002.parquet::left:1270-1270:branch_discontinuity — MEDIUM — endpoint_se3 — 42.333–42.333s

view_middleview_leftview_right

x (mm)412.90268.28y (mm)-221.43-325.82z (mm)118.2383.74step (deg)25.870.3141.33s43.33s
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old_data/pushT/meta/2026-06-18/episode_002.parquet::left:1281-1281:branch_discontinuity — MEDIUM — endpoint_se3 — 42.700–42.700s

view_middleview_leftview_right

x (mm)412.90291.35y (mm)-221.43-325.82z (mm)134.3683.74step (deg)25.870.5841.70s43.70s
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old_data/pushT/meta/2026-06-18/episode_002.parquet::left:1301-1301:branch_discontinuity — MEDIUM — endpoint_se3 — 43.367–43.367s

view_middleview_leftview_right

x (mm)412.90291.35y (mm)-221.43-325.82z (mm)156.5883.74step (deg)25.870.1142.37s44.37s
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old_data/pushT/meta/2026-06-18/episode_002.parquet::left:1370-1370:branch_discontinuity — MEDIUM — endpoint_se3 — 45.667–45.667s

view_middleview_leftview_right

x (mm)372.65260.96y (mm)-192.53-293.29z (mm)163.7092.06step (deg)15.110.0744.67s46.67s
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old_data/pushT/meta/2026-06-18/episode_002.parquet::left:1401-1401:branch_discontinuity — MEDIUM — endpoint_se3 — 46.700–46.700s

view_middleview_leftview_right

x (mm)334.55250.17y (mm)-192.53-300.32z (mm)134.5295.14step (deg)18.530.6945.70s47.70s
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old_data/pushT/meta/2026-06-18/episode_002.parquet::left:1420-1430:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 47.333–47.667s

view_middleview_leftview_right

x (mm)327.81247.26y (mm)-211.15-300.32z (mm)134.5295.49step (deg)18.530.6446.33s48.67s
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old_data/pushT/meta/2026-06-18/episode_002.parquet::left:1571-1581:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 52.367–52.700s

view_middleview_leftview_right

x (mm)375.83164.54y (mm)-235.29-318.92z (mm)150.4691.24step (deg)19.460.1851.37s53.70s
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+    "replaced_frames": 10,
+    "right_anchor": 1582,
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+    "translation_residual_p95_mm": 80.07472681544769
+  }
+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:1611-1620:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 53.700–54.000s

view_middleview_leftview_right

x (mm)375.83203.46y (mm)-235.29-355.06z (mm)150.4695.71step (deg)19.460.1852.70s55.00s
{
+  "source_start_row": 1611,
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+  "local_end_row": 1620,
+  "capture_start_seconds": 89.64164972305298,
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+  "kind": "branch_discontinuity",
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+    "duration_frames": 10,
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+    "fit_iterations": 2,
+    "left_anchor": 1610,
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+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 10,
+    "right_anchor": 1621,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 2.1507328495177425,
+    "rotation_residual_p95_deg": 22.112253866893223,
+    "translation_inlier_threshold_mm": 6.288793837096922,
+    "translation_residual_p95_mm": 87.05240250924916
+  }
+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:2150-2150:branch_discontinuity — MEDIUM — endpoint_se3 — 71.667–71.667s

view_middleview_leftview_right

x (mm)332.28156.17y (mm)-248.23-265.88z (mm)198.44125.44step (deg)15.870.0970.67s72.67s
{
+  "source_start_row": 2150,
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+  "local_end_row": 2150,
+  "capture_start_seconds": 110.06495141983032,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "fit_converged": true,
+    "fit_iterations": 0,
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+    "left_anchor": 2149,
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+    "prediction_rotation_max_deg": 9.447982036942772,
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+    "replaced_frames": 1,
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+    "right_anchor": 2151,
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+    "rotation_inlier_threshold_deg": 2.8068386146515185,
+    "rotation_residual_p95_deg": 6.427491697038621,
+    "translation_inlier_threshold_mm": 13.695036445582588,
+    "translation_residual_p95_mm": 29.079500061912082
+  }
+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:2180-2180:branch_discontinuity — MEDIUM — endpoint_se3 — 72.667–72.667s

view_middleview_leftview_right

x (mm)351.84156.17y (mm)-238.26-268.04z (mm)198.4499.86step (deg)16.130.0971.67s73.67s
{
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+    "translation_residual_p95_mm": 31.130368176651007
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:370-370:branch_discontinuity — MEDIUM — endpoint_se3 — 12.333–12.333s

view_middleview_leftview_right

x (mm)377.08270.95y (mm)-231.59-300.40z (mm)121.8485.69step (deg)28.870.3111.33s13.33s
{
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+    "rotation_inlier_threshold_deg": 11.45861274192712,
+    "rotation_residual_p95_deg": 15.991115069279855,
+    "translation_inlier_threshold_mm": 20.0,
+    "translation_residual_p95_mm": 45.269610125019646
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:4940-4940:branch_discontinuity — MEDIUM — endpoint_se3 — 164.667–164.667s

view_middleview_leftview_right

x (mm)300.01223.75y (mm)-260.78-277.63z (mm)160.0584.04step (deg)36.550.00163.67s165.67s
{
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+    "rotation_residual_p95_deg": 33.30163711454856,
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+    "translation_residual_p95_mm": 8.541980553780292
+  }
+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:581-581:branch_discontinuity — MEDIUM — endpoint_se3 — 19.367–19.367s

view_middleview_leftview_right

x (mm)308.78170.91y (mm)-254.94-286.51z (mm)208.67109.26step (deg)18.280.1118.37s20.37s
{
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+    "fit_converged": true,
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+    "left_anchor": 580,
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+    "right_anchor": 582,
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+    "rotation_inlier_threshold_deg": 7.7351669532820395,
+    "rotation_residual_p95_deg": 9.461906923434485,
+    "translation_inlier_threshold_mm": 20.0,
+    "translation_residual_p95_mm": 32.233541933263254
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:6958-6959:branch_review — MEDIUM — paired_body_branch_review — 231.933–231.967s

view_middleview_leftview_right

x (mm)374.00255.01y (mm)-236.02-326.50z (mm)121.9792.49step (deg)38.760.16230.93s232.97s
{
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+  "capture_start_seconds": 273.51821208000183,
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+  "kind": "branch_review",
+  "evidence": {
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+    "proposal_end": 6959,
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+    "right_anchor": 6960,
+    "superseded_events_json": "[{\"event_id\": \"left:6958-6959:branch_discontinuity\", \"bout\": {\"side\": \"left\", \"start\": 6958, \"end\": 6959, \"kind\": \"branch_discontinuity\", \"seed_frames\": [6958, 6959], \"left_context_end\": 6957, \"right_context_start\": 6960}, \"confidence\": 2, \"method\": \"endpoint_se3\", \"replaced_frames\": [6958, 6959], \"evidence\": {\"left_anchor\": 6957, \"right_anchor\": 6960, \"anchor_translation_mm\": 2.8513133536630115, \"anchor_rotation_deg\": 3.4223251732466067, \"prediction_translation_max_mm\": 0.9504377845543371, \"prediction_rotation_max_deg\": 1.140775057748869, \"left_context_frames\": 15, \"right_context_frames\": 15, \"duration_frames\": 2, \"observed_inlier_frames\": 0, \"replaced_frames\": 2, \"translation_residual_p95_mm\": 8.388578081826447, \"rotation_residual_p95_deg\": 38.03971929580541, \"reconstructed_max_step_translation_mm\": 0.9504377845544106, \"reconstructed_max_step_rotation_deg\": 1.1407750577474012, \"physical\": true, \"fit_iterations\": 0, \"fit_converged\": true, \"translation_inlier_threshold_mm\": 9.529113166192166, \"rotation_inlier_threshold_deg\": 3.135872587866679, \"branch_unambiguous\": true, \"endpoint_return_consistent\": true, \"independent_loss_evidence\": false, \"review_required\": true}}]",
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+    "transform_translation_disagreement_mm": 2.4321438541974727
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:740-740:branch_discontinuity — MEDIUM — endpoint_se3 — 24.667–24.667s

view_middleview_leftview_right

x (mm)302.09136.64y (mm)-196.92-296.05z (mm)134.7887.87step (deg)20.760.2223.67s25.67s
{
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+  "capture_start_seconds": 37.1241614818573,
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 739,
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+    "translation_residual_p95_mm": 6.251181165012721
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:761-771:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 25.367–25.700s

view_middleview_leftview_right

x (mm)323.81136.64y (mm)-191.52-296.05z (mm)134.7892.56step (deg)22.940.4724.37s26.70s
{
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "fit_iterations": 2,
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+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 11,
+    "right_anchor": 772,
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+    "rotation_inlier_threshold_deg": 3.2766600536949593,
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:7687-7690:branch_review — MEDIUM — paired_body_branch_review — 256.233–256.333s

view_middleview_leftview_right

x (mm)362.68133.38y (mm)-183.75-270.42z (mm)111.1894.30step (deg)35.700.14255.23s257.33s
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+    "right_anchor": 7691,
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:7737-7742:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 257.900–258.067s

view_middleview_leftview_right

x (mm)292.07136.94y (mm)-194.79-253.33z (mm)149.0794.30step (deg)38.500.30256.90s259.07s
{
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+  "capture_start_seconds": 299.68703532218933,
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+  "video_time_basis": "local_row / 30 fps",
+  "kind": "branch_discontinuity",
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+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 5,
+    "right_anchor": 7743,
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+    "rotation_inlier_threshold_deg": 4.048374239493661,
+    "rotation_residual_p95_deg": 37.49673913522744,
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+    "translation_residual_p95_mm": 14.113847268028257
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:831-840:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 27.700–28.000s

view_middleview_leftview_right

x (mm)393.67177.84y (mm)-195.92-288.47z (mm)124.6392.18step (deg)33.150.2326.70s29.00s
{
+  "source_start_row": 831,
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+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "fit_iterations": 2,
+    "left_anchor": 830,
+    "left_context_frames": 15,
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+    "prediction_rotation_max_deg": 61.94879285695945,
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+    "reconstructed_max_step_rotation_deg": 7.412263044552312,
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+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 10,
+    "right_anchor": 841,
+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 4.310529893713085,
+    "rotation_residual_p95_deg": 32.628306037670036,
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+    "translation_residual_p95_mm": 69.54383802689115
+  }
+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:851-851:branch_discontinuity — MEDIUM — endpoint_se3 — 28.367–28.367s

view_middleview_leftview_right

x (mm)393.67186.27y (mm)-209.17-290.08z (mm)124.6392.18step (deg)33.150.2327.37s29.37s
{
+  "source_start_row": 851,
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+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "fit_converged": true,
+    "fit_iterations": 0,
+    "left_anchor": 850,
+    "left_context_frames": 15,
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+    "prediction_rotation_max_deg": 10.197673527230211,
+    "prediction_translation_max_mm": 44.38330309682069,
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+    "reconstructed_max_step_translation_mm": 44.38330309682069,
+    "replaced_frames": 1,
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+    "right_context_frames": 15,
+    "rotation_inlier_threshold_deg": 5.083835409665059,
+    "rotation_residual_p95_deg": 8.454055089447683,
+    "translation_inlier_threshold_mm": 15.51782500814719,
+    "translation_residual_p95_mm": 36.44684077906916
+  }
+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:8721-8721:branch_discontinuity — MEDIUM — endpoint_se3 — 290.700–290.700s

view_middleview_leftview_right

x (mm)363.57246.73y (mm)-204.16-235.13z (mm)188.3080.32step (deg)19.990.00289.70s291.70s
{
+  "source_start_row": 8721,
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+    "fit_iterations": 0,
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+    "reconstructed_max_step_rotation_deg": 9.996116650259976,
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+    "replaced_frames": 1,
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+    "right_anchor": 8722,
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+    "rotation_inlier_threshold_deg": 2.0,
+    "rotation_residual_p95_deg": 9.996116650260143,
+    "translation_inlier_threshold_mm": 3.0,
+    "translation_residual_p95_mm": 46.91661827654571
+  }
+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:880-880:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 29.333–29.333s

view_middleview_leftview_right

x (mm)405.41186.27y (mm)-209.17-324.67z (mm)139.4196.05step (deg)23.240.0028.33s30.33s
{
+  "source_start_row": 880,
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+    "fit_iterations": 0,
+    "left_anchor": 879,
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+    "prediction_rotation_max_deg": 11.335435494464031,
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+    "reconstructed_max_step_translation_mm": 50.300470056351585,
+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 881,
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+    "rotation_residual_p95_deg": 11.946724888943935,
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+    "translation_residual_p95_mm": 53.80966630613897
+  }
+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:891-901:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 29.700–30.033s

view_middleview_leftview_right

x (mm)405.41186.27y (mm)-215.25-324.67z (mm)139.4190.16step (deg)23.240.0028.70s31.03s
{
+  "source_start_row": 891,
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+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "duration_frames": 11,
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+    "fit_iterations": 2,
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+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 10,
+    "right_anchor": 902,
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+    "translation_residual_p95_mm": 74.706511602077
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:90-90:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 3.000–3.000s

view_middleview_leftview_right

x (mm)351.13173.32y (mm)-159.96-247.47z (mm)175.8683.13step (deg)20.650.042.00s4.00s
{
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+    "rejection": "nonphysical_reconstruction",
+    "replaced_frames": 1,
+    "right_anchor": 91,
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+    "rotation_residual_p95_deg": 9.824532573819504,
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+    "translation_residual_p95_mm": 65.82924248106721
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:941-950:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 31.367–31.667s

view_middleview_leftview_right

x (mm)357.78197.09y (mm)-173.84-297.50z (mm)136.5490.16step (deg)14.130.2130.37s32.67s
{
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+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 10,
+    "right_anchor": 951,
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::left:971-990:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 32.367–33.000s

view_middleview_leftview_right

x (mm)332.87185.65y (mm)-173.84-263.71z (mm)237.1596.03step (deg)34.090.0731.37s34.00s
{
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+    "replaced_frames": 20,
+    "right_anchor": 991,
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+    "rotation_residual_p95_deg": 34.28591038267104,
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:1020-1020:branch_discontinuity — MEDIUM — endpoint_se3 — 34.000–34.000s

view_middleview_leftview_right

x (mm)559.77300.33y (mm)-199.32-267.03z (mm)150.0883.27step (deg)38.280.4933.00s35.00s
{
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+    "left_anchor": 1019,
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+    "right_anchor": 1021,
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+    "rotation_residual_p95_deg": 8.20940638046075,
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+    "translation_residual_p95_mm": 39.3222546875894
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:10239-10239:branch_discontinuity — MEDIUM — endpoint_se3 — 341.300–341.300s

view_middleview_leftview_right

x (mm)630.51518.67y (mm)-247.79-266.56z (mm)128.8985.50step (deg)20.210.00340.30s342.30s
{
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+  "capture_start_seconds": 383.74991035461426,
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+    "left_anchor": 10238,
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+    "translation_residual_p95_mm": 29.24310703104828
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:1041-1051:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 34.700–35.033s

view_middleview_leftview_right

x (mm)559.77397.31y (mm)-137.67-241.38z (mm)112.3279.73step (deg)35.870.6133.70s36.03s
{
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+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 10,
+    "right_anchor": 1052,
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+    "rotation_inlier_threshold_deg": 3.5821203488124507,
+    "rotation_residual_p95_deg": 15.11687455301304,
+    "translation_inlier_threshold_mm": 13.75595703733666,
+    "translation_residual_p95_mm": 61.729438574726075
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:10480-10480:branch_discontinuity — MEDIUM — endpoint_se3 — 349.333–349.333s

view_middleview_leftview_right

x (mm)566.33388.19y (mm)-100.75-153.22z (mm)129.6379.29step (deg)12.490.00348.33s350.33s
{
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+  "capture_start_seconds": 391.91429710388184,
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+  "evidence": {
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+    "left_anchor": 10479,
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+    "prediction_rotation_max_deg": 6.245929793305921,
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+    "right_anchor": 10481,
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+    "translation_inlier_threshold_mm": 8.481260780961705,
+    "translation_residual_p95_mm": 17.752797345862888
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:1070-1080:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 35.667–36.000s

view_middleview_leftview_right

x (mm)555.59335.43y (mm)-93.54-230.99z (mm)110.6679.19step (deg)35.870.5234.67s37.00s
{
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+  "kind": "branch_discontinuity",
+  "evidence": {
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+    "duration_frames": 11,
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+    "fit_iterations": 2,
+    "left_anchor": 1069,
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+    "prediction_rotation_max_deg": 38.11624098152243,
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+    "rejection": "incompatible_two_sided_prediction",
+    "replaced_frames": 10,
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:1111-1121:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 37.033–37.367s

view_middleview_leftview_right

x (mm)523.33335.43y (mm)-81.55-280.42z (mm)195.1079.19step (deg)19.270.4936.03s38.37s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:1151-1151:branch_discontinuity — MEDIUM — endpoint_se3 — 38.367–38.367s

view_middleview_leftview_right

x (mm)523.33358.72y (mm)-73.94-280.42z (mm)195.1096.18step (deg)18.620.3637.37s39.37s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:1180-1190:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 39.333–39.667s

view_middleview_leftview_right

x (mm)523.33399.61y (mm)-73.94-268.73z (mm)211.4096.18step (deg)16.050.3638.33s40.67s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:1241-1241:branch_discontinuity — MEDIUM — endpoint_se3 — 41.367–41.367s

view_middleview_leftview_right

x (mm)529.25449.90y (mm)-218.32-290.26z (mm)168.5295.39step (deg)14.980.1940.37s42.37s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:1261-1261:branch_discontinuity — MEDIUM — endpoint_se3 — 42.033–42.033s

view_middleview_leftview_right

x (mm)532.78389.54y (mm)-218.32-319.86z (mm)148.6295.39step (deg)15.510.1941.03s43.03s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:1281-1300:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 42.700–43.333s

view_middleview_leftview_right

x (mm)532.78372.68y (mm)-218.32-360.74z (mm)137.1874.06step (deg)15.510.1641.70s44.33s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:131-131:branch_discontinuity — MEDIUM — endpoint_se3 — 4.367–4.367s

view_middleview_leftview_right

x (mm)535.79372.08y (mm)-229.99-310.30z (mm)135.8782.11step (deg)20.230.843.37s5.37s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:1341-1370:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 44.700–45.667s

view_middleview_leftview_right

x (mm)569.15334.17y (mm)-246.28-379.39z (mm)118.7974.06step (deg)29.270.1043.70s46.67s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:1391-1391:branch_discontinuity — MEDIUM — endpoint_se3 — 46.367–46.367s

view_middleview_leftview_right

x (mm)534.27334.17y (mm)-246.28-331.33z (mm)120.9099.66step (deg)22.820.4045.37s47.37s
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:1451-1451:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 48.367–48.367s

view_middleview_leftview_right

x (mm)447.48352.13y (mm)-251.24-327.10z (mm)118.0185.68step (deg)16.860.3847.37s49.37s
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:1591-1591:branch_discontinuity — MEDIUM — endpoint_se3 — 53.033–53.033s

view_middleview_leftview_right

x (mm)465.08327.53y (mm)-264.41-325.42z (mm)179.5680.03step (deg)22.480.0452.03s54.03s
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:1610-1610:branch_discontinuity — MEDIUM — endpoint_se3 — 53.667–53.667s

view_middleview_leftview_right

x (mm)465.08327.53y (mm)-264.41-325.42z (mm)179.5680.03step (deg)22.480.1552.67s54.67s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:2060-2060:branch_discontinuity — MEDIUM — endpoint_se3 — 68.667–68.667s

view_middleview_leftview_right

x (mm)479.59343.04y (mm)-235.63-302.93z (mm)142.27106.53step (deg)20.560.0467.67s69.67s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:2121-2121:branch_discontinuity — MEDIUM — endpoint_se3 — 70.700–70.700s

view_middleview_leftview_right

x (mm)490.39353.81y (mm)-230.24-345.67z (mm)216.0493.97step (deg)30.830.2369.70s71.70s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:2141-2150:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 71.367–71.667s

view_middleview_leftview_right

x (mm)475.60353.80y (mm)-237.12-345.67z (mm)216.0477.50step (deg)30.830.3870.37s72.67s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:2171-2190:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 72.367–73.000s

view_middleview_leftview_right

x (mm)531.90353.80y (mm)-211.35-345.67z (mm)161.2677.50step (deg)30.830.3371.37s74.00s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:221-221:branch_discontinuity — MEDIUM — endpoint_se3 — 7.367–7.367s

view_middleview_leftview_right

x (mm)549.00383.06y (mm)-254.03-298.38z (mm)119.1382.70step (deg)20.140.406.37s8.37s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:301-320:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 10.033–10.667s

view_middleview_leftview_right

x (mm)462.75382.32y (mm)-238.07-326.84z (mm)130.9890.96step (deg)22.020.169.03s11.67s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:4958-4958:branch_review — MEDIUM — paired_body_branch_review — 165.267–165.267s

view_middleview_leftview_right

x (mm)483.49464.94y (mm)-245.61-286.37z (mm)97.7073.59step (deg)173.250.00164.27s166.27s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:600-600:branch_discontinuity — MEDIUM — endpoint_se3 — 20.000–20.000s

view_middleview_leftview_right

x (mm)487.56322.67y (mm)-219.82-393.60z (mm)235.5083.63step (deg)33.590.5219.00s21.00s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:621-621:branch_discontinuity — MEDIUM — endpoint_se3 — 20.700–20.700s

view_middleview_leftview_right

x (mm)487.56322.67y (mm)-270.16-393.60z (mm)208.5883.63step (deg)33.590.5719.70s21.70s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:6434-6436:branch_discontinuity — MEDIUM — endpoint_se3 — 214.467–214.533s

view_middleview_leftview_right

x (mm)583.50475.68y (mm)-247.46-307.32z (mm)116.0981.15step (deg)47.700.20213.47s215.53s
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:8606-8606:branch_discontinuity — MEDIUM — endpoint_se3 — 286.867–286.867s

view_middleview_leftview_right

x (mm)515.15390.41y (mm)-209.14-286.98z (mm)159.5984.95step (deg)13.330.00285.87s287.87s
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:8645-8645:branch_discontinuity — MEDIUM — endpoint_se3 — 288.167–288.167s

view_middleview_leftview_right

x (mm)504.88391.66y (mm)-209.31-278.01z (mm)166.7984.95step (deg)21.610.00287.17s289.17s
{
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:8944-8944:branch_discontinuity — MEDIUM — endpoint_se3 — 298.133–298.133s

view_middleview_leftview_right

x (mm)539.28399.65y (mm)-211.87-300.76z (mm)137.3676.55step (deg)12.510.32297.13s299.13s
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+}
old_data/pushT/meta/2026-06-18/episode_002.parquet::right:91-110:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 3.033–3.667s

view_middleview_leftview_right

x (mm)507.83322.40y (mm)-229.99-350.19z (mm)165.9182.11step (deg)27.020.022.03s4.67s
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old_data/pushT/meta/2026-06-18/episode_002.parquet::right:991-1000:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 33.033–33.333s

view_middleview_leftview_right

x (mm)550.85300.33y (mm)-202.36-267.03z (mm)155.0683.77step (deg)38.280.0432.03s34.33s
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+}
old_data/pushT/meta/2026-06-18/episode_003.parquet::left:12788-12789:branch_review — MEDIUM — paired_body_branch_review — 426.267–426.300s

view_middleview_leftview_right

x (mm)194.93131.84y (mm)-234.32-252.97z (mm)200.06178.95step (deg)32.300.02425.27s427.30s
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old_data/pushT/meta/2026-06-18/episode_003.parquet::left:14411-14412:branch_discontinuity — MEDIUM — endpoint_se3 — 480.367–480.400s

view_middleview_leftview_right

x (mm)248.82203.00y (mm)-252.57-263.31z (mm)113.1479.26step (deg)32.950.04479.37s481.40s
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old_data/pushT/meta/2026-06-18/episode_003.parquet::left:2985-2995:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 99.500–99.833s

view_middleview_leftview_right

x (mm)273.94138.87y (mm)-151.27-417.12z (mm)267.7287.51step (deg)66.660.0098.50s100.83s
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old_data/pushT/meta/2026-06-18/episode_003.parquet::left:3126-3126:branch_discontinuity — MEDIUM — endpoint_se3 — 104.200–104.200s

view_middleview_leftview_right

x (mm)263.45202.74y (mm)-246.22-404.33z (mm)262.73138.42step (deg)59.920.03103.20s105.20s
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old_data/pushT/meta/2026-06-18/episode_003.parquet::left:3138-3138:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 104.600–104.600s

view_middleview_leftview_right

x (mm)320.87202.74y (mm)-223.17-401.68z (mm)262.2291.49step (deg)59.920.03103.60s105.60s
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old_data/pushT/meta/2026-06-18/episode_003.parquet::left:3686-3686:branch_review — MEDIUM — paired_body_branch_review — 122.867–122.867s

view_middleview_leftview_right

x (mm)268.83237.00y (mm)-201.28-224.75z (mm)114.7979.93step (deg)33.120.13121.87s123.87s
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old_data/pushT/meta/2026-06-18/episode_003.parquet::left:7271-7271:branch_review — MEDIUM — paired_body_branch_review — 242.367–242.367s

view_middleview_leftview_right

x (mm)252.14232.15y (mm)-225.43-267.64z (mm)165.86145.02step (deg)37.860.16241.37s243.37s
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old_data/pushT/meta/2026-06-18/episode_003.parquet::left:7286-7295:branch_review — MEDIUM — paired_body_branch_review — 242.867–243.167s

view_middleview_leftview_right

x (mm)252.14207.22y (mm)-244.43-267.64z (mm)183.2989.39step (deg)37.860.16241.87s244.17s
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old_data/pushT/meta/2026-06-18/episode_003.parquet::right:11257-11257:branch_discontinuity — MEDIUM — endpoint_se3 — 375.233–375.233s

view_middleview_leftview_right

x (mm)548.50505.83y (mm)-237.31-259.96z (mm)136.8173.78step (deg)13.230.00374.23s376.23s
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old_data/pushT/meta/2026-06-18/episode_003.parquet::right:3737-3737:branch_discontinuity — MEDIUM — endpoint_se3 — 124.567–124.567s

view_middleview_leftview_right

x (mm)499.42489.77y (mm)-201.57-221.96z (mm)116.9471.85step (deg)19.150.00123.57s125.57s
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old_data/pushT/meta/2026-06-18/episode_003.parquet::right:5879-5879:branch_discontinuity — MEDIUM — endpoint_se3 — 195.967–195.967s

view_middleview_leftview_right

x (mm)451.09391.40y (mm)-262.49-297.11z (mm)154.3499.01step (deg)14.870.00194.97s196.97s
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old_data/pushT/meta/2026-06-18/episode_003.parquet::right:7140-7140:branch_discontinuity — MEDIUM — endpoint_se3 — 238.000–238.000s

view_middleview_leftview_right

x (mm)509.81488.16y (mm)-221.05-234.26z (mm)117.4272.95step (deg)14.880.00237.00s239.00s
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old_data/pushT/meta/2026-06-18/episode_003.parquet::right:7372-7372:branch_discontinuity — MEDIUM — endpoint_se3 — 245.733–245.733s

view_middleview_leftview_right

x (mm)429.76404.03y (mm)-221.44-310.15z (mm)170.99134.04step (deg)18.120.00244.73s246.73s
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old_data/pushT/meta/2026-06-18/episode_003.parquet::right:8156-8156:branch_discontinuity — MEDIUM — endpoint_se3 — 271.867–271.867s

view_middleview_leftview_right

x (mm)497.72375.89y (mm)-282.83-450.34z (mm)200.9965.10step (deg)13.040.22270.87s272.87s
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+}
old_data/pushT/meta/2026-06-18/episode_003.parquet::right:9858-9858:branch_discontinuity — MEDIUM — endpoint_se3 — 328.600–328.600s

view_middleview_leftview_right

x (mm)574.46382.88y (mm)-202.07-270.66z (mm)168.9069.75step (deg)29.580.00327.60s329.60s
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+}
\ No newline at end of file diff --git a/action_sample25.html b/action_sample25.html new file mode 100644 index 0000000000000000000000000000000000000000..825b45d34ba35ebee84f5d0d8122904074dda2f2 --- /dev/null +++ b/action_sample25.html @@ -0,0 +1,885 @@ +Mocap event review

25 examples for human verification

25 selected event occurrences across published segments. Raw traces are red; repaired traces are blue. Missing values are gaps. Each chart includes one second of context; long intervals are sampled. Video links and charts use segment-local frame / 30 fps; capture-time offsets are listed separately. No approval is implied. The measured camera footage is unchanged; before/after repair is shown in the red/blue pose charts. These examples support human verification and are not proof that every jump is tracking loss. Check visible hand/object motion against the measured jump, the surrounding frames, and the repair.

data/motherboard/meta/2026-09-11/episode_007_seg00.parquet::right:544-544:branch_discontinuity — MEDIUM — endpoint_se3 — 18.133–18.133s — UNVERIFIED

view_middlewrist_rightview_leftview_right

view_middle

wrist_right

x (mm)361.04291.57y (mm)-311.53-354.90z (mm)127.8959.61step (deg)15.030.0017.13s19.13s
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+  }
+}
data/rope/meta/2026-09-14/episode_003_seg01.parquet::right:3960-3964:branch_discontinuity — MEDIUM — endpoint_se3 — 69.500–69.633s — UNVERIFIED

view_middlewrist_rightview_leftview_right

view_middle

wrist_right

x (mm)447.06368.76y (mm)-243.98-404.63z (mm)125.0671.65step (deg)75.710.3468.50s70.63s
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+}
data/pushT/meta/2026-09-10/episode_003_seg02.parquet::left:10247-10248:branch_discontinuity — MEDIUM — endpoint_se3 — 0.000–0.033s — UNVERIFIED

view_middlewrist_leftview_leftview_right

view_middle

wrist_left

x (mm)207.48100.14y (mm)-213.62-281.40z (mm)144.40112.48step (deg)122.780.000.00s1.03s
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+}
data/toy/meta/2026-09-14/episode_000_seg00.parquet::left:6525-6529:branch_discontinuity — MEDIUM — endpoint_se3 — 33.700–33.700s — UNVERIFIED

view_middlewrist_leftview_leftview_right

view_middle

wrist_left

x (mm)196.53166.79y (mm)-233.95-261.42z (mm)225.7682.43step (deg)65.180.3332.70s33.70s
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data/validation/meta/2026-09-09/episode_001.parquet::right:1394-1397:branch_discontinuity — MEDIUM — endpoint_se3 — 46.467–46.567s — UNVERIFIED

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view_middle

wrist_right

x (mm)459.55382.50y (mm)-296.01-359.18z (mm)136.0879.50step (deg)151.490.0945.47s47.57s
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old_data/motherboard/meta/2026-05-10/episode_000.parquet::left:6729-6729:branch_discontinuity — MEDIUM — endpoint_se3 — 224.300–224.300s — UNVERIFIED

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view_middle

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old_data/pushT/meta/2026-06-18/episode_001.parquet::left:11024-11027:branch_discontinuity — MEDIUM — endpoint_se3 — 367.467–367.567s — UNVERIFIED

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view_middle

x (mm)353.21181.21y (mm)-109.27-185.03z (mm)115.1136.31step (deg)140.520.35366.47s368.57s
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data/motherboard/meta/2026-09-11/episode_007_seg02.parquet::left:11508-11524:known_gap — HIGH — robust_se3_hermite — 180.267–180.800s — UNVERIFIED

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view_middle

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x (mm)80.3669.06y (mm)-237.62-261.09z (mm)155.36119.49step (deg)116.210.02179.27s181.80s
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data/validation/meta/2026-09-09/episode_003.parquet::right:7010-7015:branch_discontinuity — MEDIUM — robust_se3_hermite — 233.667–233.833s — UNVERIFIED

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view_middle

wrist_right

x (mm)433.09409.45y (mm)-239.68-336.74z (mm)198.92160.67step (deg)134.330.00232.67s234.83s
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data/pushT/meta/2026-09-10/episode_004_seg00.parquet::left:568-571:branch_discontinuity — MEDIUM — endpoint_se3 — 5.433–5.533s — UNVERIFIED

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x (mm)257.64177.25y (mm)-213.76-249.78z (mm)148.93105.79step (deg)101.620.374.43s6.53s
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data/rope/meta/2026-09-11/episode_000_seg00.parquet::left:2114-2116:branch_discontinuity — MEDIUM — endpoint_se3 — 70.467–70.533s — UNVERIFIED

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view_middle

wrist_left

x (mm)290.19235.01y (mm)-323.47-383.93z (mm)115.1153.54step (deg)95.500.1569.47s71.53s
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data/toy/meta/2026-09-14/episode_000_seg00.parquet::right:5938-5938:branch_discontinuity — MEDIUM — endpoint_se3 — 14.133–14.133s — UNVERIFIED

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view_middle

wrist_right

x (mm)345.92226.18y (mm)-286.65-503.78z (mm)102.1467.57step (deg)39.200.2813.13s15.13s
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old_data/motherboard/meta/2026-05-10/episode_002.parquet::left:10364-10364:branch_discontinuity — MEDIUM — endpoint_se3 — 345.467–345.467s — UNVERIFIED

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view_middle

x (mm)590.4164.60y (mm)-284.25-329.98z (mm)158.2080.60step (deg)12.450.17344.47s346.47s
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old_data/pushT/meta/2026-06-18/episode_001.parquet::left:7981-7984:branch_discontinuity — MEDIUM — endpoint_se3 — 266.033–266.133s — UNVERIFIED

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data/validation/meta/2026-09-09/episode_005.parquet::right:1081-1087:branch_discontinuity — MEDIUM — robust_se3_hermite — 36.033–36.233s — UNVERIFIED

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view_middle

wrist_right

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data/motherboard/meta/2026-09-11/episode_007_seg04.parquet::right:24330-24330:branch_discontinuity — MEDIUM — endpoint_se3 — 10.033–10.033s — UNVERIFIED

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view_middle

wrist_right

x (mm)697.81431.16y (mm)-86.22-378.56z (mm)272.11121.43step (deg)18.520.009.03s11.03s
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+}
data/pushT/meta/2026-09-10/episode_004_seg02.parquet::right:10779-10781:branch_discontinuity — MEDIUM — endpoint_se3 — 36.933–37.000s — UNVERIFIED

view_middlewrist_rightview_leftview_right

view_middle

wrist_right

x (mm)471.48334.62y (mm)-219.85-318.98z (mm)146.8497.77step (deg)122.910.3135.93s38.00s
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+}
data/rope/meta/2026-09-11/episode_000_seg00.parquet::left:816-819:branch_discontinuity — MEDIUM — endpoint_se3 — 27.200–27.300s — UNVERIFIED

view_middlewrist_leftview_leftview_right

view_middle

wrist_left

x (mm)165.3991.03y (mm)-289.59-320.65z (mm)105.5959.06step (deg)115.720.1026.20s28.30s
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+}
data/toy/meta/2026-09-14/episode_000_seg03.parquet::right:15106-15107:branch_discontinuity — MEDIUM — endpoint_se3 — 57.200–57.233s — UNVERIFIED

view_middlewrist_rightview_leftview_right

view_middle

wrist_right

x (mm)471.05404.47y (mm)-151.71-162.89z (mm)133.3097.43step (deg)56.330.0056.20s57.23s
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+}
old_data/motherboard/meta/2026-05-11/episode_005.parquet::right:10454-10454:branch_discontinuity — MEDIUM — endpoint_se3 — 348.467–348.467s — UNVERIFIED

view_middleview_leftview_right

view_middle

x (mm)785.94535.36y (mm)-291.93-553.85z (mm)243.09131.98step (deg)18.620.00347.47s349.47s
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+}
data/motherboard/meta/2026-09-11/episode_007_seg04.parquet::left:24381-24421:gap_with_discontinuity — LOW — unrepaired_ambiguous_branch — 11.733–13.067s — UNVERIFIED

view_middlewrist_leftview_leftview_right

view_middle

wrist_left

x (mm)195.0539.39y (mm)-124.42-459.50z (mm)174.5273.21step (deg)13.910.0010.73s14.07s
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+}
old_data/motherboard/meta/2026-05-11/episode_013.parquet::left:8050-8112:known_gap — LOW — unrepaired_ambiguous_branch — 268.333–270.400s — UNVERIFIED

view_middleview_leftview_right

view_middle

x (mm)85.1716.24y (mm)-491.70-519.58z (mm)141.3266.39step (deg)136.840.00267.33s271.40s
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+}
old_data/pushT/meta/2026-06-18/episode_001.parquet::left:9752-9758:branch_review — MEDIUM — paired_body_branch_review — 325.067–325.267s — UNVERIFIED

view_middleview_leftview_right

view_middle

x (mm)297.78144.57y (mm)-201.97-222.31z (mm)134.1195.68step (deg)36.240.08324.07s326.27s
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+}
data/validation/meta/2026-09-09/episode_003.parquet::left:6412-6439:branch_discontinuity — LOW — unrepaired_ambiguous_branch — 213.733–214.633s — UNVERIFIED

view_middlewrist_leftview_leftview_right

view_middle

wrist_left

x (mm)183.68162.11y (mm)-269.32-362.89z (mm)256.43207.43step (deg)34.210.07212.73s215.63s
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+}
old_data/motherboard/meta/2026-05-11/episode_015.parquet::left:708-768:known_gap — LOW — unrepaired_ambiguous_branch — 23.600–25.600s — UNVERIFIED

view_middleview_leftview_right

view_middle

x (mm)197.4322.84y (mm)-151.43-405.39z (mm)79.8838.40step (deg)121.060.0522.60s26.60s
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+    "translation_residual_p95_mm": 92.30468278281418
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+}
\ No newline at end of file diff --git a/action_sample25.json b/action_sample25.json new file mode 100644 index 0000000000000000000000000000000000000000..d3923576a3e477e9c070d9c87b0260222365ec10 --- /dev/null +++ b/action_sample25.json @@ -0,0 +1,23883 @@ +{ + "requested_count": 25, + "actual_count": 25, + "high_count": 1, + "sample_group_counts": { + "algorithmic_proposal": 20, + "other_uncertain": 5 + }, + "scope_counts": { + "data/motherboard": 4, + "data/rope": 3, + "data/pushT": 3, + "data/toy": 3, + "data/validation": 4, + "old_data/motherboard": 5, + "old_data/pushT": 3 + }, + "selection": "Prefer 20 algorithmic proposals (HIGH/prior HIGH or explicitly lacking independent loss evidence) and 5 other uncertain; balance scopes, durations, kinds; deduplicate source events. All require human verification.", + "limitation": "Human verification examples, not proof that every jump is tracking loss.", + "events": [ + { + "confidence": "MEDIUM", + "end": 544, + "event_id": "right:544-544:branch_discontinuity", + "evidence": { + "anchor_rotation_deg": 14.981120961953765, + "anchor_translation_mm": 48.37311204519902, + "branch_unambiguous": true, + "duration_frames": 1, + "endpoint_return_consistent": true, + "fit_converged": true, + "fit_iterations": 0, + "independent_loss_evidence": false, + "left_anchor": 543, + "left_context_frames": 15, + "observed_inlier_frames": 0, + "physical": true, + "prediction_rotation_max_deg": 7.490560480976883, + "prediction_translation_max_mm": 24.18655602259951, + "reconstructed_max_step_rotation_deg": 7.490560480976786, + "reconstructed_max_step_translation_mm": 24.18655602259951, + "replaced_frames": 1, + "review_required": true, + "right_anchor": 545, + "right_context_frames": 15, + "rotation_inlier_threshold_deg": 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