--- license: mit task_categories: - robotics tags: - robotics - manipulation - bimanual - dexterous-manipulation - mujoco - dexjoco - data-generation - dexmimicgen size_categories: - 1K **These are generated, not human-recorded.** Every episode comes from warping one > of the 100 official teleop demos to a fresh layout and rolling it out in MuJoCo. > The on-disk format is byte-format identical to > [`DexJoCo/DexJoCo-Datasets-Raw`](https://huggingface.co/datasets/DexJoCo/DexJoCo-Datasets-Raw) > so it drops into the same tooling, but do not mistake it for the official raw data. Task prompt: *"Grasp the tray with the left hand and the peg with the right hand, then insert the peg into the hole."* ## Layout ``` dexjoco_raw_datasets/bimanual_assembly//replay.zarr/ data/{action (T,46) f64, action_rotvec (T,44) f64, state (T,1,61) f32, timestamp (T,) f64} meta/episode_ends dexjoco_raw_datasets/bimanual_assembly//videos/{ego,wrist_left,wrist_right}.mp4 metadata/episode_provenance.json # per episode: source demo + step counts ``` `state` layout (61-d): `tcp_pose(14) gripper_pose(32) socket_ori_pose(7) peg_ori_pose(7) table_delta_height(1)`. Videos are 640x640 H.264, 30 fps, one frame per timestep. Same `zstd`/`BITSHUFFLE` zarr codecs as the official assembly data. Load an episode: ```python import zarr, numpy as np g = zarr.open("dexjoco_raw_datasets/bimanual_assembly//replay.zarr", mode="r") state = np.asarray(g["data"]["state"])[:, 0] # (T, 61) action = np.asarray(g["data"]["action"]) # (T, 46) ``` Convert to LeRobot / Zarr with the `dexjoco-data-converter` in the DexJoCo repo. ## How it was generated Per trial: sample a new layout (`env.reset()` — peg xy in a 5x5 cm box with +/-10 deg yaw, tray xy in a 10x10 cm box with +/-20 deg yaw, table height ~ U(0, 5 cm)); pick the source demo whose peg pose is nearest (top-3, random); warp each arm's end-effector trajectory by the per-subtask SE(3) delta and replay the 16-DoF Allegro hand joints verbatim; finish the insertion with a closed-loop servo onto the live socket-bottom geom; keep the rollout if the env reports success (peg/socket-bottom contact sustained 30 steps). Rendering is on during generation, so the written episode is exactly the rollout that was validated. **Yield: 1842 successes / 9785 trials = 18.8%.** The residual failures are dominated by angular peg/hole misalignment introduced by the warp. From that pool, 1000 episodes were quota-sampled so the episode-length histogram matches the official demos'. ## How closely it matches the official data | property | official (100 demos) | this set (1000 episodes) | |---|---|---| | episode length | median 550 steps / 18.3 s, mean 582, range 431-1069 | median 559 / 18.6 s, mean 601, range 446-1170 | | length distribution | — | two-sample KS **0.114** vs official (crit 0.143 at a=0.05) | | peg x / y layout | U(bounds) | 100% in bounds, uniformity KS 0.035 / 0.038 (crit 0.043) | | tray x / y layout | U(bounds) | 100% in bounds, KS 0.018 / 0.026 | | peg / tray yaw, table height | U(bounds) | 100% in bounds, KS 0.074 / 0.056 / 0.057 — mildly skewed | | storage format | — | **68/68** properties identical (zarr keys, shapes, dtypes, codecs, filters, fill_value, order, attrs, `episode_ends`, 30 Hz timestamps, `action_rotvec == recompute(action)`, video names/codec/resolution/pix_fmt/fps/frame-count) | | content | — | identical start pose, object block constant within an episode, unit-norm action quaternions, per-step motion + TCP tracking percentiles in the official range | Known deviations: the orientation axes and table height keep a mild skew from the success filter (angular misalignment is the dominant failure mode, so success is not orientation- neutral); commanded end-effector poses reach slightly outside the official command envelope (z to 1.726 vs 1.701 m); and 87 of the 100 official demos appear as sources, the most frequent contributing 68 episodes. ## Provenance and reproduction `metadata/episode_provenance.json` maps every episode to the official source demo it was warped from, with both step counts. The generator, the production driver (`scripts/datagen_produce.py`), the selection step (`scripts/datagen_select_balanced.py`) and the audit tooling (`dexjoco/datagen/audit/`) live on the `datagen` branch of the DexJoCo repo, and `dexjoco/datagen/RESULTS.md` records the full measurement log. ## License MIT, following the DexJoCo repository. The source demonstrations are the official DexJoCo teleoperation data; please cite the DexJoCo paper if you use this set. ```bibtex @article{dexjoco, title = {DexJoCo: A Unified Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo}, url = {https://arxiv.org/abs/2605.16257}, year = {2026} } ```