| --- |
| license: cc-by-nc-sa-4.0 |
| language: |
| - en |
| task_categories: |
| - robotics |
| tags: |
| - robotics |
| - robot-learning |
| - bimanual-manipulation |
| - video |
| - webdataset |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/agibot/train/*.tar |
| - split: validation |
| path: data/agibot/validation/*.tar |
| --- |
| |
| # AgiBot World Beta processed episodes |
|
|
| A processed subset of [agibot-world/AgiBotWorld-Beta](https://huggingface.co/datasets/agibot-world/AgiBotWorld-Beta), |
| containing **142,685 verified episodes**, approximately **2,330.1 hours**, and **6.22 TB** of exported data. |
| Credit for the original demonstrations and annotations belongs to the AgiBot World contributors. |
| This is a derivative release. |
|
|
| | Split | Episodes | Hours | |
| | --- | ---: | ---: | |
| | Training | 128,530 | 2,098.7 | |
| | Validation | 14,155 | 231.4 | |
|
|
| This subset requires all three camera streams, robot state/action data, camera intrinsics, |
| and task labels. Episodes with more than 1 cm of base translation are excluded, as are |
| three source episodes with unreadable head-camera video. All retained source frames are preserved. |
| The source dataset contains additional episodes and modalities outside this processed subset. |
| The source annotations do not provide episode success/failure labels. |
|
|
| ## Format |
|
|
| Uncompressed WebDataset TAR shards of approximately 4 GiB are stored under |
| `data/agibot/train/` and `data/agibot/validation/`. Each episode has a stable key and three members: |
|
|
| - `<key>.mp4`: H.264 video at 30 fps. The head, left-wrist, and right-wrist camera views |
| are each resized to 640 × 480 and stacked vertically into a 640 × 1440 frame. |
| - `<key>.bin`: contiguous, row-major, little-endian float64 state/action values. |
| - `<key>.json`: task instructions, scene description, subtask annotations, frame counts, |
| column layout, camera intrinsics, and public source episode identifiers. |
|
|
| Each binary row corresponds to one video frame. The 32 columns contain left-arm state |
| (7 joints and gripper), right-arm state (7 joints and gripper), and the corresponding |
| left/right action values. The exact order is provided in `row_layout.columns`. |
| Grippers are normalized to 0 = closed and 1 = open. Joint values are in radians. |
| Source joint-action values match the joint-state values and are preserved as recorded; |
| gripper actions use the recorded commands. No additional action lag is introduced. |
| Camera intrinsics describe the individual resized views; see per-episode metadata. |
|
|
| 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"], 32) |
| ``` |
|
|
| `dataset_info.json` summarizes the release. `shards.jsonl` provides archive sizes, |
| episode counts, and SHA-256 checksums. Episode metadata contains only public data fields. |
|
|
| ## Quality-control audit |
|
|
| A full audit of **142,685 episodes** and **754,950,021 camera frames** |
| is published in [`audit/`](audit), covering the retained processed frames at revision |
| `6fc8f88d74250c492622e7ff03586e162b852b2f`. It includes joint, video, pose-continuity, workspace, |
| tracking/lag and duplicate checks, with explicit applicability limits. |
|
|
| **96,315 episodes triggered a configured threshold**, including |
| **1,450 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. Independent AgiBot FK could not be checked without a verified robot model. 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. |
|
|
| AgiBot retains the source robot/ego-frame flange state poses. All 142,685 episodes |
| have valid finite state-pose arrays. Native EEF commands exactly copy state poses |
| in 141,318 episodes. Of the remaining episodes, 1,364 have shorter command streams |
| and three have distinct dense commands. Sparse commands are aligned only at valid |
| source indices; the fifty episodes with unresolved indices have pose-command masks |
| set to false. Uncommanded rows are not silently padded with state poses. |
|
|
| For the OpenWAM-Alpha future-state recipe, select `action_mode="next_state"`. |
| `command` mode preserves the native command distinction and its missing-data masks; |
| it cannot recover independent commands from the source copies. Gripper commands |
| remain separate. The exact URDF root origin and dataset-wide visual projection |
| accuracy have not been independently certified. |
|
|
| 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="next_state" |
| ) |
| # 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. |
|
|
| ## License and attribution |
|
|
| This processed release retains the upstream **Creative Commons Attribution-NonCommercial-ShareAlike |
| 4.0 International (CC BY-NC-SA 4.0)** license. See `LICENSE` and the |
| [license terms](https://creativecommons.org/licenses/by-nc-sa/4.0/). |
| Noncommercial use, attribution, and sharing adaptations under the same license are required. |
|
|
| The original source is [AgiBotWorld-Beta](https://huggingface.co/datasets/agibot-world/AgiBotWorld-Beta) |
| from the [AgiBot World contributors](https://github.com/OpenDriveLab/AgiBot-World). |
| Changes in this release include video resizing and encoding, state/action formatting, |
| gripper normalization, selection of stationary-base episodes, dataset splits, metadata reduction, |
| and archive packaging. |
|
|
| Please cite the original project: AgiBot World Colosseum contributors (2024), |
| *AgiBot World Colosseum*, https://github.com/OpenDriveLab/AgiBot-World. |
|
|
