OneWorld-AI/AgiBotWorld-Beta-processed

AgiBot World Beta processed episodes

A processed subset of 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:

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.

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:

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. Noncommercial use, attribution, and sharing adaptations under the same license are required.

The original source is AgiBotWorld-Beta from the AgiBot World contributors. 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.