| --- |
| license: mit |
| task_categories: |
| - robotics |
| tags: |
| - robotics |
| - manipulation |
| - bimanual |
| - dexterous-manipulation |
| - mujoco |
| - dexjoco |
| - data-generation |
| - dexmimicgen |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # DexJoCo bimanual_assembly — 1000 generated demonstrations |
| |
| 1000 successful `bimanual_assembly` demonstrations for the |
| [DexJoCo](https://dexjoco.github.io) benchmark, produced by SE(3)-warping the |
| 100 official human teleoperation demos onto newly randomized object layouts and |
| keeping the rollouts the simulator reports as successful (a DexMimicGen-style |
| data-generation pipeline). |
|
|
| > **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/<episode>/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/<episode>/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/<episode>/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} |
| } |
| ``` |
|
|
