Keep trajectory comparisons in chat; reserve the visualizer for annotations

README.md

@@ -80,12 +80,8 @@ The Grabette reference comparison still showed 12–20 cm initial-frame position
 RMSE on those four clips (8–12 cm after rigid position alignment, without a scale
 fit). Its existing trajectories are SLAM-derived references. This is an
 experimental reconstruction, not validated robot pose ground truth.
-Open `/?trajectory=owner/output-dataset` to rotate the 3D trajectory overlay,
-choose an episode and inspect its error; private results use your Studio login.
-Add `&compare=owner/other-output-dataset` to overlay another run. Comparison
-requires identical source actions, frame indices and timestamps; both runs are
-shown against the source reference with scale fixed at one. The optional rigid
-alignment is a diagnostic full-trajectory fit and is labeled explicitly.
+Trajectory comparisons and calibration diagnostics are reviewed in chat.
+The Space visualizer is dedicated to dataset annotations.
 
 **Hand actions** uses EgoHand with native SOMA/MHR hands and
 **DROID-W + MoGe Small** inside the existing annotation wizard. Choose a human-POV

frontend/src/JobsView.tsx

@@ -47,7 +47,6 @@ export type Progress = {
   };
   annotation_url?: string | null;
   hand_visualizer_url?: string | null;
-  slam_visualizer_url?: string | null;
   output_mode?: string;
   human_videos?: Record<string, number> | null;
 };
@@ -241,8 +240,7 @@ export default function JobsView({
                 : null;
             const annotations = p?.annotation_url;
             const hands = p?.hand_visualizer_url;
-            const slam = p?.slam_visualizer_url;
-            const action = slam ? "View 3D trajectories" : hands
+            const action = hands
               ? "View 3D hands"
               : annotations
                 ? "Show annotations"
@@ -313,7 +311,7 @@ export default function JobsView({
                   </span>
                   <span className="compact-job-action">
                     <a
-                      href={slam || hands || annotations || results || logs}
+                      href={hands || annotations || results || logs}
                       target="_blank"
                       rel="noreferrer"
                       onClick={(e) => e.stopPropagation()}

frontend/src/TrajectoryPreview.tsx

@@ -1,76 +0,0 @@
-import {useEffect, useState} from "react";
-import "./trajectory.css";
-
-type Point = [number,number,number];
-type Track = {dataset:string;predicted:Point[];rigid_aligned:Point[]|null;rmse:{initial_mm:number;rigid_mm:number}|null};
-type Trajectory = Track & {episode:number;total_episodes:number;frames:number;fps:number;frame_indices:number[];reference:Point[]|null;reference_note:string;comparison?:Track};
-
-export default function TrajectoryPreview({dataset}:{dataset:string}) {
-  const [data,setData]=useState<Trajectory|null>(null);
-  const [episode,setEpisode]=useState(0);
-  const [error,setError]=useState("");
-  const [angle,setAngle]=useState([-.65,.45]);
-  const [zoom,setZoom]=useState(1);
-  const [aligned,setAligned]=useState(false);
-  const [frame,setFrame]=useState(0);
-  const [compare,setCompare]=useState(new URLSearchParams(window.location.search).get("compare") || "");
-  const [compareDraft,setCompareDraft]=useState(compare);
-  useEffect(()=>{
-    let active=true;
-    setData(null);setError("");
-    fetch(`/api/slam-trajectory?dataset=${encodeURIComponent(dataset)}&episode=${episode}${compare?`&compare=${encodeURIComponent(compare)}`:""}`,{credentials:"same-origin"})
-      .then(async r=>{const d=await r.json();if(!r.ok)throw new Error(d.detail||"Could not load trajectory");return d as Trajectory;})
-      .then(d=>{if(active){setData(d);setFrame(d.predicted.length-1);}})
-      .catch(e=>{if(active)setError(e.message);});
-    return ()=>{active=false;};
-  },[dataset,episode,compare]);
-  const prediction=data && aligned && data.rigid_aligned ? data.rigid_aligned : data?.predicted;
-  const comparison=data?.comparison && aligned && data.comparison.rigid_aligned ? data.comparison.rigid_aligned : data?.comparison?.predicted;
-  const points=[...(prediction||[]),...(comparison||[]),...(data?.reference||[])];
-  const center=[0,1,2].map(axis=>points.length ? points.reduce((s,p)=>s+p[axis],0)/points.length : 0);
-  const extent=Math.max(.05,...points.flatMap(p=>p.map((v,i)=>Math.abs(v-center[i]))));
-  const scale=165/extent*zoom;
-  function project(p:number[]):[number,number] {
-    const [x,y,z]=p.map((v,i)=>v-center[i]);
-    const a=x*Math.cos(angle[0])+z*Math.sin(angle[0]);
-    const b=-x*Math.sin(angle[0])+z*Math.cos(angle[0]);
-    return [350+a*scale,240+(y*Math.cos(angle[1])-b*Math.sin(angle[1]))*scale];
-  }
-  const path=(values:Point[])=>values.slice(0,frame+1).map((p,i)=>`${i?"L":"M"}${project(p).join(",")}`).join(" ");
-  return <main className="trajectory-preview">
-    <header><a href="/">← Annotation Studio</a><a href={`/auth/login?next=${encodeURIComponent(window.location.pathname+window.location.search)}`}>Sign in for private datasets</a></header>
-    <h1>DROID-W and dataset trajectories</h1><p className="field-help">{dataset}</p>
-    <form className="trajectory-controls" onSubmit={e=>{e.preventDefault();setCompare(compareDraft.trim());const url=new URL(window.location.href);if(compareDraft.trim())url.searchParams.set("compare",compareDraft.trim());else url.searchParams.delete("compare");window.history.replaceState(null,"",url);}}>
-      <label>Compare another run of the same episodes<input type="text" value={compareDraft} placeholder="owner/dataset-without-intrinsics" onChange={e=>setCompareDraft(e.target.value)}/></label><button type="submit">Compare in 3D</button>
-    </form>
-    <div className="trajectory-controls">
-      <label>Episode <input type="number" min={0} max={data ? data.total_episodes-1 : undefined} value={episode} onChange={e=>setEpisode(Math.max(0,Number(e.target.value)))}/></label>
-      <label>Alignment <select value={aligned?"rigid":"initial"} onChange={e=>setAligned(e.target.value==="rigid")}><option value="initial">Initial camera frames</option><option value="rigid">Rigid position alignment · no scale fit</option></select></label>
-      <label>Zoom <input type="range" min="0.5" max="3" step="0.05" value={zoom} onChange={e=>setZoom(Number(e.target.value))}/></label>
-    </div>
-    {error && <p role="alert" className="message error">{error}</p>}
-    {!data && !error && <p role="status">Loading 3D trajectories…</p>}
-    {data && <>
-      <div className="trajectory-legend"><span className="trajectory-droid">━━ {compare?"Selected run":"DROID-W"}</span>{comparison && <span className="trajectory-comparison">━ · Comparison run</span>}{data.reference && <span className="trajectory-reference">┄┄ Dataset reference</span>}<span>Metres · X right · Y down · Z forward</span></div>
-      {data.comparison && <p className="field-help">Comparison: {data.comparison.dataset}</p>}
-      <svg className="trajectory-canvas" viewBox="0 0 700 480" role="img" aria-label="Interactive 3D camera trajectory overlay; drag to rotate"
-        onPointerDown={e=>e.currentTarget.setPointerCapture(e.pointerId)}
-        onPointerMove={e=>{if(e.buttons)setAngle(a=>[a[0]+e.movementX*.008,Math.max(-1.4,Math.min(1.4,a[1]+e.movementY*.008))]);}}>
-        <title>DROID-W and source dataset camera trajectories</title>
-        {([0,1,2] as const).map(axis=>{
-          const p=[...center];p[axis]+=extent;
-          const a=project(center),b=project(p);
-          return <g key={axis}><line x1={a[0]} y1={a[1]} x2={b[0]} y2={b[1]} className="trajectory-axis"/><text x={b[0]+5} y={b[1]-5}>{["X","Y","Z"][axis]} {extent.toFixed(2)} m</text></g>;
-        })}
-        {data.reference && <path d={path(data.reference)} className="trajectory-reference-line"/>}
-        {comparison && <path d={path(comparison)} className="trajectory-comparison-line"/>}
-        {prediction && <path d={path(prediction)} className="trajectory-droid-line"/>}
-        {prediction && <circle cx={project(prediction[Math.min(frame,prediction.length-1)])[0]} cy={project(prediction[Math.min(frame,prediction.length-1)])[1]} r="4" className="trajectory-current"/>}
-      </svg>
-      <label className="trajectory-frame">Frame {data.frame_indices[frame]} / {data.frames-1}<input type="range" min="0" max={data.predicted.length-1} value={frame} onChange={e=>setFrame(Number(e.target.value))}/></label>
-      <p aria-live="polite">{data.rmse ? `Position RMSE: ${(aligned?data.rmse.rigid_mm:data.rmse.initial_mm).toFixed(1)} mm · scale fixed at 1` : "No compatible Cartesian reference pose column in this dataset."}</p>
-      {data.comparison?.rmse && <p>Comparison RMSE: {(aligned?data.comparison.rmse.rigid_mm:data.comparison.rmse.initial_mm).toFixed(1)} mm · scale fixed at 1</p>}
-      <p className="field-help">Drag to rotate. {data.reference_note}</p>
-    </>}
-  </main>;
-}

frontend/src/main.tsx

@@ -39,7 +39,6 @@ import "./style.css";
 import "./wizard.css";
 import AnnotationExamples from "./AnnotationExamples";
 import SlamSettings, { defaultSlam, type SlamOptions } from "./SlamSettings";
-import TrajectoryPreview from "./TrajectoryPreview";
 import JobsView, { type Job, type Progress } from "./JobsView";
 import HandActionSettings, {
   HandPipelineExplanation,
@@ -2211,5 +2210,4 @@ function App() {
   );
 }
 
-const trajectoryDataset = new URLSearchParams(window.location.search).get("trajectory");
-createRoot(document.getElementById("root")!).render(trajectoryDataset ? <TrajectoryPreview dataset={trajectoryDataset}/> : <App />);
+createRoot(document.getElementById("root")!).render(<App />);

frontend/src/trajectory.css

@@ -1,21 +0,0 @@
-.trajectory-preview { max-width: 1000px; margin: auto; padding: 28px; }
-.trajectory-preview header,.trajectory-controls,.trajectory-legend { display: flex; flex-wrap: wrap; align-items: center; gap: 20px; margin-bottom: 24px; }
-.trajectory-preview header { justify-content: space-between; }
-.trajectory-controls label { display: flex; flex-direction: column; gap: 6px; }
-.trajectory-controls input[type=number] { width: 100px; }
-.trajectory-preview select,.trajectory-preview input { padding: 8px; color: var(--fg); background: var(--surface-0); border: 1px solid var(--line); border-radius: 5px; }
-.trajectory-canvas { display: block; width: 100%; height: auto; touch-action: none; cursor: grab; background: var(--surface-0); }
-.trajectory-canvas:active { cursor: grabbing; }
-.trajectory-canvas text { fill: var(--fg-muted); font-size: 12px; }
-.trajectory-axis { stroke: var(--line-strong); stroke-width: 1; }
-.trajectory-reference-line { stroke: var(--green); stroke-width: 2; stroke-dasharray: 6 3; fill: none; }
-.trajectory-droid-line { stroke: var(--accent); stroke-width: 2; fill: none; }
-.trajectory-comparison-line { stroke: var(--fg); stroke-width: 2; stroke-dasharray: 9 3 2 3; fill: none; }
-.trajectory-comparison { color: var(--fg); }
-.trajectory-controls input[type=text] { width: min(70vw, 440px); }
-.trajectory-droid { color: var(--accent); }
-.trajectory-reference { color: var(--green); }
-.trajectory-current { fill: var(--accent); }
-.trajectory-frame { display: flex; align-items: center; gap: 16px; margin-top: 20px; }
-.trajectory-frame input { flex: 1; }
-@media(max-width:480px) { .trajectory-preview {padding:16px;} .trajectory-controls {align-items:stretch;flex-direction:column;} .trajectory-frame {flex-wrap:wrap;} }

server.py

@@ -24,7 +24,6 @@ from prompt_config import public_catalog, validate_prompts
 from pydantic import BaseModel, Field
 from reporting import annotation_url, episode_progress, hand_visualizer_url, usage_costs
 from slam_pipeline import catalog as slam_catalog
-from slam_view import trajectory_data
 from studio import COPY, FEATURES, launch, prepare, read_cameras
 
 app = FastAPI(title="LeRobot Annotation Studio", docs_url=None, redoc_url=None, openapi_url=None)
@@ -183,10 +182,6 @@ def login(request: Request):
     if not client_id or not HOST:
         return RedirectResponse("/?error=local-preview")
     sid, current = create_session()
-    destination = request.query_params.get("next", "/")
-    current["auth_return"] = (
-        destination if destination.startswith("/?trajectory=") and len(destination) < 1024 else "/"
-    )
     current["oauth_state"] = secrets.token_urlsafe(32)
     current["verifier"] = secrets.token_urlsafe(64)
     current["auth_started"] = time.time()
@@ -220,7 +215,6 @@ def callback(request: Request, code: str = "", state: str = "", error: str = "")
     ):
         return RedirectResponse("/?error=sign-in")
     verifier = current.pop("verifier", "")
-    destination = current.pop("auth_return", "/")
     current.pop("oauth_state", None)
     try:
         result = requests.post(
@@ -248,7 +242,7 @@ def callback(request: Request, code: str = "", state: str = "", error: str = "")
             user={"name": user["name"], "fullname": user.get("fullname") or user["name"]},
             expires=time.time() + min(int(data.get("expires_in", 86400)), 86400),
         )
-    return cookie(RedirectResponse(destination), sid)
+    return cookie(RedirectResponse("/"), sid)
 
 
 @app.post("/auth/logout")
@@ -350,19 +344,6 @@ def slam_pipeline():
     return slam_catalog()
 
 
-@app.get("/api/slam-trajectory")
-def slam_trajectory(request: Request, dataset: str, episode: int = 0, compare: str | None = None):
-    current = session(request) or {}
-    if episode < 0 or not ESTIMATES.acquire(blocking=False):
-        raise HTTPException(400, "Choose a valid episode, or retry when other previews finish")
-    try:
-        return trajectory_data(dataset, episode, current.get("token", False), compare_dataset=compare)
-    except Exception as exc:
-        raise HTTPException(400, safe_error(exc)) from None
-    finally:
-        ESTIMATES.release()
-
-
 @app.post("/api/prompts/validate")
 def check_prompts(options: PromptInput, request: Request):
     check_origin(request)
@@ -484,9 +465,6 @@ def progress(job_id: str, request: Request):
             "cost": usage_costs(job, report),
             "annotation_url": annotation_url(report),
             "hand_visualizer_url": hand_visualizer_url(report),
-            "slam_visualizer_url": "/?" + urlencode({"trajectory": output})
-            if report.get("manifest", {}).get("slam")
-            else None,
             "output_mode": report.get("manifest", {}).get("mode"),
             "model": report.get("manifest", {}).get("model"),
             "captioner": report.get("manifest", {}).get("captioner"),

slam-pipeline.json

@@ -3,12 +3,11 @@
   "coordinate_frame": "initial optical camera; X right, Y down, Z forward",
   "expected_code": {
     "grabette-camera.json": "9ce2f5b0211184faafbe549b1b60d70ebea66c5459682d49f55ae5e69e4c8532",
-    "server.py": "b7a1f176054468b9180eeb7bcd6482676870cf3838e79ae49a61dca492ef386f",
+    "server.py": "5322b54c4002959b8b28251f4997c299d3921e7663680d8819bb577a181698b9",
     "slam_job.py": "25449ada3483cd2ba6851db334b1614df2809a7ae1640a7788ad218a05fd5cd6",
     "slam_native.py": "2199bf30d0a06583e25493149663edcdc5869e94d32b900f5db3f9b94912ee62",
     "slam_parallel.py": "254bf2e4f3e1872d34413b1c6c467b82aafbd975eed3f67e89bb699bc81b582b",
-    "slam_pipeline.py": "4ac5fe3e0a54e18065f718eda2db68b3f594d7384b8073675e5b92f52ccc3962",
-    "slam_view.py": "32092424288d07e3be2308b789d4a1e93d15a42e6120aee842539de2dc898968",
+    "slam_pipeline.py": "8b3b1777519ccaa90f9f4613060f5c634092566e7fda4d085d1675f61308243d",
     "studio.py": "ffe3cddf755f4999c7e5d5f790805c74fbb37aa93eed24d777ca8dda64accb41",
     "subset.py": "c32c62ca23155c602a4c92e67a76613bc2c4dec4ddf9dd4dc554600bb99034d3"
   },

slam_pipeline.py

@@ -13,7 +13,6 @@ FILES = {
     "slam_pipeline.py",
     "slam_job.py",
     "slam_native.py",
-    "slam_view.py",
     "slam_parallel.py",
     "server.py",
     "studio.py",

slam_view.py

@@ -1,95 +0,0 @@
-"""Numerical, permission-checked trajectory previews for saved SLAM datasets."""
-
-import hashlib
-import json
-
-import numpy as np
-import pyarrow.parquet as pq
-from huggingface_hub import hf_hub_download
-
-
-def trajectory_data(dataset, episode, token=False, compare_dataset=None):
-    """Return bounded 3D paths; never expose the caller's Hub credentials."""
-    from studio import read_selection, relative_path
-
-    selection = read_selection(dataset, episode, 1, token=token)
-    row, info = selection["episodes"][0], selection["info"]
-    if int(row["episode_index"]) != episode:
-        raise ValueError("The selected episode does not exist")
-    name = relative_path(
-        info["data_path"].format(
-            chunk_index=int(row["data/chunk_index"]), file_index=int(row["data/file_index"])
-        )
-    )
-    path = hf_hub_download(dataset, name, repo_type="dataset", revision=selection["revision"], token=token)
-    table = pq.read_table(path, filters=[("episode_index", "=", episode)])
-    column = "annotation.camera_initial_from_optical"
-    if column not in table.column_names:
-        raise ValueError("This dataset has no saved standalone SLAM camera poses")
-    predicted = np.asarray(table[column].to_pylist(), dtype=float)
-    if predicted.shape != (len(table), 4, 4) or not np.isfinite(predicted).all():
-        raise ValueError("Invalid saved camera trajectory")
-    predicted = np.linalg.inv(predicted[0]) @ predicted
-    reference, aligned, rmse = None, None, None
-    names = info["features"].get("action", {}).get("names", [])
-    if names[:9] == ["x", "y", "z", *[f"r6d_{i}" for i in range(6)]]:
-        values = np.asarray(table["action"].to_pylist(), dtype=float)
-        a, b = values[:, 3:6], values[:, 6:9]
-        a = a / np.linalg.norm(a, axis=1)[:, None]
-        b = b - (a * b).sum(1)[:, None] * a
-        b = b / np.linalg.norm(b, axis=1)[:, None]
-        rotation = np.stack([a, b, np.cross(a, b)], axis=1)
-        reference = (values[:, :3] - values[0, :3]) @ rotation[0]
-        valid = np.isfinite(reference).all(axis=1)
-        if "is_lost" in table.column_names:
-            valid &= np.asarray(table["is_lost"].to_pylist()) < 0.5
-        if valid.sum() >= 8:
-            p, r = predicted[:, :3, 3], reference
-            p0, r0 = p[valid].mean(0), r[valid].mean(0)
-            u, _, vt = np.linalg.svd((p[valid] - p0).T @ (r[valid] - r0))
-            transform = vt.T @ np.diag([1.0, 1.0, np.linalg.det(vt.T @ u.T)]) @ u.T
-            aligned = (p - p0) @ transform.T + r0
-            rmse = {
-                "initial_mm": float(1000 * np.sqrt(np.mean(np.sum((p[valid] - r[valid]) ** 2, axis=1)))),
-                "rigid_mm": float(1000 * np.sqrt(np.mean(np.sum((aligned[valid] - r[valid]) ** 2, axis=1)))),
-            }
-    keep = np.unique(np.rint(np.linspace(0, len(table) - 1, min(2000, len(table)))).astype(int))
-    fingerprint = hashlib.sha256(
-        json.dumps(
-            {
-                name: table[name].to_pylist()
-                for name in ("action", "timestamp", "frame_index")
-                if name in table.column_names
-            },
-            allow_nan=False,
-        ).encode()
-    ).hexdigest()
-    result = {
-        "dataset": dataset,
-        "revision": selection["revision"],
-        "episode": episode,
-        "total_episodes": info["total_episodes"],
-        "frames": len(table),
-        "fps": info["fps"],
-        "frame_indices": keep.tolist(),
-        "predicted": predicted[keep, :3, 3].tolist(),
-        "reference": reference[keep].tolist() if reference is not None else None,
-        "rigid_aligned": aligned[keep].tolist() if aligned is not None else None,
-        "rmse": rmse,
-        "scale_fitted": False,
-        "source_fingerprint": fingerprint,
-        "reference_note": "Existing dataset pose estimates, normalized to their first pose. Camera mounting and metric accuracy are not established by this overlay.",
-    }
-    if compare_dataset:
-        other = trajectory_data(compare_dataset, episode, token=token)
-        if (
-            reference is None
-            or other["reference"] is None
-            or fingerprint != other["source_fingerprint"]
-            or info["fps"] != other["fps"]
-        ):
-            raise ValueError("Compare matching source episodes with identical actions, frames and timestamps")
-        result["comparison"] = {
-            key: other[key] for key in ("dataset", "revision", "predicted", "rigid_aligned", "rmse")
-        }
-    return result

test_slam.py

@@ -114,58 +114,3 @@ def test_grabette_preset_scales_fisheye_without_changing_source():
     assert result["calibration"]["revision"] == "48b9a5c3706c197954b2cd9502b036b87d9e9abb"
     with pytest.raises(ValueError, match="aspect ratio"):
         grabette_calibration({"shape": [360, 640, 3]})
-
-
-def test_saved_3d_preview_retains_scale_error(tmp_path):
-    from slam_view import trajectory_data
-
-    count = 20
-    poses = np.tile(np.eye(4), (count, 1, 1))
-    poses[:, 0, 3] = np.arange(count) * 0.02
-    actions = [[i * 0.01, 0, 0, 1, 0, 0, 0, 1, 0] for i in range(count)]
-    path = tmp_path / "episode.parquet"
-    import pyarrow.parquet as pq
-
-    pq.write_table(pa.table({"episode_index": [0] * count, POSE: poses.tolist(), "action": actions}), path)
-    selection = {
-        "revision": "a" * 40,
-        "episodes": [{"episode_index": 0, "data/chunk_index": 0, "data/file_index": 0}],
-        "info": {
-            "data_path": "data/{file_index}.parquet",
-            "fps": 50,
-            "total_episodes": 1,
-            "features": {"action": {"names": ["x", "y", "z", *[f"r6d_{i}" for i in range(6)]]}},
-        },
-    }
-    with (
-        patch("studio.read_selection", return_value=selection),
-        patch("slam_view.hf_hub_download", return_value=str(path)) as download,
-    ):
-        result = trajectory_data("alice/output", 0, token=False)
-    assert result["scale_fitted"] is False
-    assert result["rmse"]["rigid_mm"] > 50
-    assert download.call_args.kwargs["token"] is False
-    with (
-        patch("studio.read_selection", return_value=selection),
-        patch("slam_view.hf_hub_download", return_value=str(path)),
-    ):
-        overlay = trajectory_data("alice/calibrated", 0, token=False, compare_dataset="alice/baseline")
-    assert overlay["comparison"]["dataset"] == "alice/baseline"
-    assert overlay["comparison"]["predicted"] == overlay["predicted"]
-    changed = tmp_path / "different-episode.parquet"
-    pq.write_table(
-        pa.table(
-            {
-                "episode_index": [0] * count,
-                POSE: poses.tolist(),
-                "action": [[v + 0.1 for v in a] for a in actions],
-            }
-        ),
-        changed,
-    )
-    with (
-        patch("studio.read_selection", return_value=selection),
-        patch("slam_view.hf_hub_download", side_effect=[str(path), str(changed)]),
-    ):
-        with pytest.raises(ValueError, match="matching source episodes"):
-            trajectory_data("alice/calibrated", 0, compare_dataset="alice/different")