robopark/dexjoco-single-arm-data

DexJoCo single-arm image datasets

Processed demonstrations for all six single-arm DexJoCo tasks, ready for the DICE-RL image behavior-cloning loader. This public release contains 600 episodes and 219,938 retained transitions. All 100 available raw recordings per task passed conversion; no episodes were rejected. No policy checkpoints or measured policy success rates are included.

Task	Episodes	Transitions	Retained episode length	Archive GiB
click_mouse	100	32,702	192–774	1.03
fold_glasses	100	53,878	282–844	1.91
hammer_nail	100	21,563	149–522	1.02
pick_bucket	100	43,581	233–1055	1.81
pinch_tongs	100	40,469	288–680	1.10
water_plant	100	27,745	203–394	1.10

Total archive size: 7.97 GiB. Allow approximately 15.9 GiB for cached archives plus extracted data.

Download

Use the updated scripts in stpark7/dice-rl, with huggingface_hub installed in the activated Python environment:

bash script/download_hf.sh

No token or Hugging Face login is required. The destination is $DICE_RL_DATA_DIR/dexjoco if that variable is set, otherwise the checkout's data_dir/dexjoco. To select tasks or a destination:

bash script/download_hf.sh --task click_mouse --task water_plant --data-dir /path/to/data

The optional dexjoco selector is supported. The script resolves one commit for the whole download, verifies each archive's SHA-256, and installs each task atomically. Record the printed commit or use --revision <commit> to reproduce a release. Existing unrelated datasets are preserved; use a new data directory if the target task directory already exists without a matching download receipt. A repeat download checks the archive and receipt, but does not re-audit edited local arrays. Original Robomimic downloads use bash script/download_hf.sh robomimic.

data_dir/dexjoco/<task>-img/
├── episodes.json
├── ph_pretrain/
│   ├── train.npz
│   └── normalization.npz
└── ph_finetune/
    ├── train.npz
    └── normalization.npz

Each NPZ contains states, actions, traj_lengths, camera_names, and images_<camera> for every recorded camera. Finetuning additionally includes rewards and terminals. Images are embedded in both training files, so each NPZ is self-contained. This is a custom NPZ/tar layout rather than a datasets.load_dataset table. The complete file index, sizes, checksums and per-task statistics are in manifest.json.

Data and conversion

• All cameras are retained in this order: ego_left, ego_right, front, wrist. Their arrays are RGB uint8, shaped (T, 3, 96, 96).
• States are float32, 23-dimensional [tcp_pose7, hand_joints16]. Pose quaternions use wxyz ordering.
• Actions are float32, 22-dimensional: world translation delta (3), world relative rotation vector (3), and absolute hand-joint targets (16). The rotation increment left-multiplies the measured TCP orientation.
• States and actions use per-task min/max normalization to approximately [-1, 1] with epsilon 1e-6, fitted on all retained episodes. There is no reserved validation/test split. Images are not normalized by the loader.
• Leading zero-command markers are removed. Initial held-target prefixes are trimmed after sustained target departure: position 0.001 m, rotation 0.5°, hand joints 0.01 rad, 3 consecutive samples, minimum held duration 0.5 s, retained context 0.1 s. Interior pauses remain intact.
• MP4 frame t is assumed to align with recorded state/action sample t. Every camera's decoded frame count must equal the original state count; the same prefix cut is applied to all modalities. The recordings do not independently establish camera synchronization through timestamps.
• Episode ordering, original/retained lengths, prefix cuts and thresholds are recorded in episodes.json. Published source paths are relative to the raw dataset root.
• ph_finetune uses the same sequences and images as ph_pretrain, with a synthetic reward/terminal of 1 at every retained episode end and 0 elsewhere. Recorded task-success labels are not imported. These synthetic rewards do not certify demonstration or policy success.

Choose cameras explicitly in train_dataset.image_keys, for example [front, wrist]; the loader decompresses only those camera members. Keep this order consistent with the environment, model num_img and image shape. The older mixed single-arm/bimanual repository is a separate release and is unchanged by this publication.

Verification

All six tasks were freshly converted from the local raw recordings using the current converter defaults. Before packaging:

• All 600 episodes' stored states and actions were compared exactly against fresh raw re-encoding, the recorded prefix cuts and the published normalizers. The encoder is shared with the converter; this is a consistency check rather than an independent action-encoding implementation.
• All shared arrays in both training files, including 879,752 retained camera frames, matched exactly. Shapes, dtypes, finite values, episode boundaries, normalization extrema and synthetic rewards were checked. No all-black camera frames were found.
• 216 camera frames were compared pixel-for-pixel with fresh raw MP4 decoding: first/middle/last retained frames in first/middle/last episodes, for every task and camera. There were no mismatches.
• Each task passed a CPU BC-loader sample check using [front, wrist], an eight-step action horizon and one conditioning frame.
• Converter, action-contract and downloader regression tests passed. Archive SHA-256 values are verified by the downloader before extraction.

Conversion/audit environment: Python 3.11.15; numpy 1.26.4, scipy 1.15.3, zarr 2.18.3, av 18.1.0, torch 2.7.0+cu128, huggingface_hub 0.36.2. Exact converter/video/action/loader source-file SHA-256 values are recorded in the manifest because the release was produced from an edited working tree.

Source and license

Original demonstrations: DexJoCo/DexJoCo-Datasets-Raw. Simulator and source documentation: brave-eai/dexjoco. Training/conversion code: stpark7/dice-rl, derived from DICE-RL.

The upstream raw dataset is published under the MIT license. The upstream copyright and license notice are included in LICENSE.

Please cite the original DexJoCo and DICE-RL work when using their data/code:

• DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo.
• From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning.