| --- |
| license: apache-2.0 |
| language: |
| - en |
| - zh |
| task_categories: |
| - robotics |
| tags: |
| - robotics |
| - robot-learning |
| - bimanual-manipulation |
| - video |
| - webdataset |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: agilex |
| data_files: |
| - split: train |
| path: data/agilex/train/*.tar |
| - split: validation |
| path: data/agilex/validation/*.tar |
| - config_name: franka |
| data_files: |
| - split: train |
| path: data/franka/train/*.tar |
| - split: validation |
| path: data/franka/validation/*.tar |
| - config_name: ur5 |
| data_files: |
| - split: train |
| path: data/ur5/train/*.tar |
| - split: validation |
| path: data/ur5/validation/*.tar |
| --- |
| |
| # RoboMIND 2.0 processed episodes |
|
|
| A processed subset of [RoboMIND 2.0](https://modelscope.cn/datasets/X-Humanoid/RoboMIND2.0) |
| containing **134,537 episodes** and approximately **846 hours** of bimanual robot demonstrations. |
| This is a derivative release, with credit to the original RoboMIND authors. |
|
|
| | Robot | Training episodes | Validation episodes | |
| | --- | ---: | ---: | |
| | AgileX | 19,027 | 2,084 | |
| | Franka | 60,069 | 6,616 | |
| | UR5 | 42,005 | 4,736 | |
| | Total | 121,101 | 13,436 | |
|
|
| The subset contains source-labeled successful demonstrations with structurally valid video and state/action streams. |
| Identical recorded payloads are deduplicated; alternate task instructions are retained in |
| `source_aliases` when present. Episodes with missing, incompatible, or frozen camera streams |
| are excluded. This release does not contain every embodiment or episode in the upstream dataset. |
|
|
| ## UR5 action-quality caveat |
|
|
| The [Qwen-RobotManip technical report, Section 2.4](https://arxiv.org/html/2606.17846#S2.SS4) |
| reports that 81% of the RoboMIND UR-type episodes in its curation corpus failed a |
| state-action trend-alignment check. The report covers both RoboMIND and RoboMIND 2.0; |
| it does not identify which episodes in this derivative release are affected. |
| **The reported 81% is not a measured failure rate for this release.** |
|
|
| An exploratory audit on October 22, 2026 checked all **46,741 exported UR5 episodes** |
| and flagged **12 (0.026%)** with at least one moving arm joint below 0.70 directional |
| agreement after lag alignment. At thresholds 0.60 and 0.65, 7 and 8 episodes were |
| flagged respectively. Two of the 12 also had a joint whose best lag was negative. |
| The median estimated joint lag was 0.278 seconds, with actions preceding states. |
|
|
| This diagnostic used five-frame triangular smoothing, a lag search of approximately |
| plus or minus one second using position correlation, and agreement of the signs of |
| lag-aligned first differences. It scored arm joints only when both streams spanned |
| at least 0.05 radians, ignored differences at or below 0.00001 radians per frame, |
| and required at least ten scored differences. Grippers and low-motion joints were |
| excluded. A separate unsmoothed check of 512 reproducibly sampled UR5 episodes |
| flagged none at the 0.70 threshold. Twenty sampled episodes and all twelve flagged |
| episodes matched the original HDF5 values after the documented format conversion. |
|
|
| **This is not an exact reproduction of the paper's filter or a certification of |
| training quality.** It does not validate video-state alignment, kinematics, task |
| labels, or controller suitability. The recorded actions remain unchanged, without |
| temporal correction. We recommend excluding the following twelve episodes from |
| action-supervised training pending individual review, and assessing the remaining |
| UR5 data for the intended training setup. |
|
|
| <details> |
| <summary>UR5 episode keys flagged for review</summary> |
|
|
| ```text |
| episode-17b9a9076462f3968e3455400ccd6d87f67b688294221a8bc274eb2a46ce594a |
| episode-2ddd2e1082689571a038f343359f8a7f5f8917b2ca6659edc8e36f50e5a0a237 |
| episode-32eb11fbaeecd0ad13cf08974126d48d30e5efaaf17aa209cb38d9674df3db31 |
| episode-4ba3a75369573c1cd79664131a70dd27566624dcfecbdb023c79ce93e19e7c09 |
| episode-4d804fcf7fd753f39626a67daf75b96f4701cb906cf46c651713a91a95bab3ff |
| episode-5f6097f7ac3569a4282e9bf0b2fe3cc4ec5bf2008b02c4f8d127754791a23322 |
| episode-7270a65a8afe8eab94b7bcd83866e531f4815b1d60e51cfe6beaceec60a89a71 |
| episode-818172b46a58b29ba874da1d7765d084e3087ddfe66a7865dd4f07843ebc97d0 |
| episode-8a48c96c88c8b345a10404db81f74efe3cde1d9bd0f72498750955c900ebcd2d |
| episode-a6d8d7020fd3f86643625c34ddcf9150bdf26164b114880c7b17c8470478f256 |
| episode-d002afc3996d3e513062e129ed1d47793b8a8868706f5d0da8a20711fae483f8 |
| episode-f2b1c676c204368342ea930dc7af795e2e332f911212d4873a36b592419f5269 |
| ``` |
|
|
| </details> |
|
|
| ## Format |
|
|
| Files are grouped into uncompressed WebDataset TAR shards of approximately 4 GiB under |
| `data/<robot>/<split>/`. Each episode has a stable key and three members: |
|
|
| - `<key>.mp4`: H.264 RGB video, with top, left-wrist, and right-wrist views stacked vertically. |
| Each view is letterboxed to 640 × 480, giving a combined frame of 640 × 1440. |
| - `<key>.bin`: contiguous, row-major, little-endian float64 state/action values. |
| - `<key>.json`: task instructions, frame rate, frame count, column layout, camera layout, |
| source episode identifiers, and other data interpretation metadata. |
|
|
| Video frame rates are capped at 30 fps, retaining lower native rates. One binary row corresponds |
| to one video frame. The exact frame rate and retained source frame indices are provided per episode. |
| AgileX and UR5 have 28 columns per row; Franka has 32. Read `row_layout.columns` for the exact order. |
| Robot joint values are in radians. Grippers are normalized to 0 = closed and 1 = open. |
| States describe the robot arms and actions describe the recorded leader arms, without added lag. |
| Camera calibration is unavailable and represented as `null`. |
|
|
| For an extracted episode: |
|
|
| ```python |
| import json |
| import numpy as np |
| |
| with open("<key>.json", encoding="utf-8") as stream: |
| metadata = json.load(stream) |
| rows = np.fromfile("<key>.bin", dtype="<f8").reshape( |
| metadata["num_steps"], metadata["row_layout"]["floats_per_row"] |
| ) |
| ``` |
|
|
| The archive members contain the exported video and state/action bytes. Episode metadata is |
| reduced to public data fields. `dataset_info.json` summarizes the release and `shards.jsonl` |
| lists archive sizes, episode counts, and SHA-256 checksums. |
|
|
| ## Quality-control audit |
|
|
| A full audit of **134,537 episodes** and **127,677,339 camera frames** |
| is published in [`audit/`](audit), covering the retained processed frames at revision |
| `72877243ff25e5f484ea216a1d684b65d1c0d7d0`. It includes joint, video, pose-continuity, workspace, |
| tracking/lag and duplicate checks, with explicit applicability limits. |
|
|
| **35,070 episodes triggered a configured threshold**, including |
| **232 EEF-state spike candidates**. Broad acceleration/jerk |
| flags are heuristic, and dark-frame detections can include camera occlusion. These |
| are **review candidates, not an automatic removal list or demonstration outcome labels**. |
| Check counts overlap; [`audit/summary.json`](audit/summary.json) records the full breakdown. |
|
|
| - [`audit/episode_flags.csv`](audit/episode_flags.csv): public episode IDs, robot, split and check reasons. |
| - [`audit/ids/`](audit/ids): aggregate and per-check ID lists, including separate EEF state/command lists. |
| - [`audit/measurements/`](audit/measurements): gzip JSONL measurements for every episode and audit pass. |
| - [`audit/methods/`](audit/methods): thresholds and training-only statistical workspace bounds. |
| - [`audit/duplicate_groups.jsonl`](audit/duplicate_groups.jsonl): canonical/duplicate IDs and cross-split indicators. |
| - [`audit/files.jsonl`](audit/files.jsonl): public audit file sizes and SHA-256 checksums. |
|
|
| Measurement JSON uses `flagged_checks`; CSV check names are separated by semicolons. |
| The CSV contains episodes with a flag or review annotation; measurement files cover |
| every episode. Frame indices are zero-based retained video indices. EEF spike |
| measurements also give source indices and affected `next_state` action indices. |
| The native-pose/FK comparisons refer to upstream recorded fields, separate from |
| the published nominal-FK targets. Copied joint commands cannot establish independent |
| tracking quality. RoboMIND native-pose/FK disagreements are review-only; published EEF poses use nominal joint FK. Physical calibration and dataset-wide camera reprojection remain unverified. |
|
|
| **No episodes, videos, joint binaries or EEF payloads were removed or repaired.** |
| The published EEF arrays retain the audited values, including flagged anomalies. |
|
|
| ## EEF action/state companions |
|
|
| The optional `eef/` companion release covers every episode in this processed subset, |
| using the same stable `episode_id` and exactly the same retained video frames. The |
| original joint binaries and videos remain available. Companion TARs contain |
| `<episode_id>.npz` arrays and `<episode_id>.json` frame/pose provenance. |
|
|
| `state_pose` is `[T, 2, 7]` float32: XYZ in metres followed by quaternion **xyzw**, |
| left arm then right arm. `command_pose` uses the same layout; when a recorded command |
| stream exactly copies the state, the array is stored once and the metadata declares |
| the alias. Gripper arrays use **0 = closed, 1 = open**. Boolean pose and gripper masks |
| must be respected. `source_frame_index` preserves the video-to-source row mapping. |
|
|
| Select action semantics explicitly in `eef/reader.py`: |
|
|
| - `action_mode="command"`: the corresponding recorded-command pose, with no added |
| timestep shift. Copied source pose commands are excluded from supervision by default. |
| - `action_mode="next_state"`: the achieved state at the next retained frame, including |
| gripper state. This is a derived target, not an original controller command. The |
| final target is masked; do not shift it again in another loader. |
|
|
| All RoboMIND EEF states and commands are calculated separately from the published |
| puppet and master joint rows. Franka uses `panda_link0` to `panda_link8`; UR5e uses |
| the manufacturer's nominal UR5e DH base to `tool0`; AgileX uses the dataset authors' |
| `aloha_new.urdf`, verified byte-identical to the manufacturer model, from each |
| `fl_base_link`/`fr_base_link` to `fl_link6`/`fr_link6`. The two arms' local base frames |
| are distinct; no missing common-base or camera calibration is fabricated. |
|
|
| The earlier UR5e rotation encoding discrepancy is avoided by reconstructing poses |
| from the corresponding joints after diagnosis. The twelve command-review episodes |
| listed above have command supervision masked out. These are nominal-model poses, |
| not factory-calibrated measurements or a certificate of camera reprojection accuracy. |
| Invalid AgileX native EEF fields are not used as ground truth or fitted calibration. |
|
|
| The reader emits `[T, 80]` state/action arrays and masks by default, with the left |
| 20-D schema arm in dimensions `0:10` and the right arm in `34:44`; the unused slots |
| are masked. Set `unify_action=False` for `[T, 20]`: each arm is XYZ + the first two |
| rotation-matrix columns (6D rotation) + gripper. Poses use the frames named in each |
| episode, not camera coordinates or a universal fingertip TCP. |
|
|
| From a downloaded dataset root, join an existing episode's metadata to its companion: |
|
|
| ```python |
| from eef.reader import load_index, load_indexed_episode |
| |
| eef_index = load_index("eef/episodes.jsonl") |
| targets = load_indexed_episode( |
| ".", eef_index[metadata["episode_id"]], action_mode="command" |
| ) |
| # targets["state"], targets["action"], targets["state_mask"], targets["action_mask"] |
| ``` |
|
|
| `eef/episodes.jsonl` supplies direct TAR offsets. `eef/shards.jsonl` supplies file |
| sizes and SHA-256 checksums; `eef/info.json` records full coverage and verification. |
| `eef/models.json` documents frames, kinematics and limitations. `eef/statistics.json` |
| contains separate state, command and next-state statistics from the **training split |
| only**; stored poses are unnormalized. Keep rotation-6D coordinates unnormalized. |
|
|
| The portable reader matches the OpenWAM pose layout; integrating these companions |
| with a training pipeline's video/window reader is still required. The original |
| OpenWAM LeRobot reader does not directly consume these WebDataset companions. |
|
|
| ## Source and license |
|
|
| Upstream sources are [X-Humanoid/RoboMIND2.0-Agilex](https://modelscope.cn/datasets/X-Humanoid/RoboMIND2.0-Agilex), |
| [RoboMIND2.0-Franka](https://modelscope.cn/datasets/X-Humanoid/RoboMIND2.0-Franka-Part-1) |
| (parts 1–5), and [RoboMIND2.0-UR5](https://modelscope.cn/datasets/X-Humanoid/RoboMIND2.0-UR5). |
| They are distributed under Apache License 2.0, retained for this processed release; see `LICENSE`. |
| Changes include video encoding and resizing, temporal sampling, state/action formatting, |
| gripper normalization, filtering, deduplication, and dataset splits. |
|
|
| Please cite the original work: |
| Chengkai Hou et al. (2025), [RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset |
| for Generalizable Embodied Intelligence](https://arxiv.org/abs/2512.24653). |
|
|
