OneWorld-AI/RoboMIND2.0-processed

RoboMIND 2.0 processed episodes

A processed subset of RoboMIND 2.0 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 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.

UR5 episode keys flagged for review

episode-17b9a9076462f3968e3455400ccd6d87f67b688294221a8bc274eb2a46ce594a
episode-2ddd2e1082689571a038f343359f8a7f5f8917b2ca6659edc8e36f50e5a0a237
episode-32eb11fbaeecd0ad13cf08974126d48d30e5efaaf17aa209cb38d9674df3db31
episode-4ba3a75369573c1cd79664131a70dd27566624dcfecbdb023c79ce93e19e7c09
episode-4d804fcf7fd753f39626a67daf75b96f4701cb906cf46c651713a91a95bab3ff
episode-5f6097f7ac3569a4282e9bf0b2fe3cc4ec5bf2008b02c4f8d127754791a23322
episode-7270a65a8afe8eab94b7bcd83866e531f4815b1d60e51cfe6beaceec60a89a71
episode-818172b46a58b29ba874da1d7765d084e3087ddfe66a7865dd4f07843ebc97d0
episode-8a48c96c88c8b345a10404db81f74efe3cde1d9bd0f72498750955c900ebcd2d
episode-a6d8d7020fd3f86643625c34ddcf9150bdf26164b114880c7b17c8470478f256
episode-d002afc3996d3e513062e129ed1d47793b8a8868706f5d0da8a20711fae483f8
episode-f2b1c676c204368342ea930dc7af795e2e332f911212d4873a36b592419f5269

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:

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.

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:

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, RoboMIND2.0-Franka (parts 1–5), and 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.