--- pretty_name: DexJoCo Single-Arm Image Data license: mit language: - en tags: - robotics - dexjoco - behavior-cloning - image-observations - single-arm source_datasets: - DexJoCo/DexJoCo-Datasets-Raw --- # 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 **596 episodes and 216,890 retained transitions**. Four demonstrations were removed before processing due to poor trajectory quality: pick_bucket demo 39 (244967) and demo 12 (974934), click_mouse demo 42 (581035), and hammer_nail demo 2 (403013). They were not replaced. This **2026-10-21** release contains 98 pick_bucket, 99 click_mouse, 99 hammer_nail, and 100 episodes for each other task. Every remaining raw recording passed conversion; no additional episodes were rejected. Per-task normalization for the three affected tasks was recomputed using only the clean retained episodes. No policy checkpoints or measured policy success rates are included. | Task | Episodes | Transitions | Retained episode length | Archive GiB | |---|---:|---:|---:|---:| | click_mouse | 99 | 31,928 | 192–538 | 1.01 | | fold_glasses | 100 | 53,878 | 282–844 | 1.91 | | hammer_nail | 99 | 21,041 | 149–392 | 0.99 | | pick_bucket | 98 | 41,829 | 233–725 | 1.73 | | pinch_tongs | 100 | 40,469 | 288–680 | 1.10 | | water_plant | 100 | 27,745 | 203–394 | 1.10 | Total archive size: **7.84 GiB**. Allow approximately **15.7 GiB** for cached archives plus extracted data. ## Download Use the updated scripts in [stpark7/dice-rl](https://github.com/stpark7/dice-rl), with `huggingface_hub` installed in the activated Python environment: ```bash 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 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 ` 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`. ```text data_dir/dexjoco/-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_` 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](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. To keep a previous local installation, download this release to a new directory: ```bash bash script/download_hf.sh --data-dir data_dir/dexjoco-clean-20261021 ``` The older mixed single-arm/bimanual repository is a separate release and is unchanged by this publication. ## Verification The three affected tasks were freshly converted from the cleaned local raw recordings using the same converter defaults. Unaffected tasks reuse the prior verified NPZ files; all six tasks were audited again against the available raw recordings before packaging: - All 596 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 **867,560 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](https://huggingface.co/datasets/DexJoCo/DexJoCo-Datasets-Raw). Simulator and source documentation: [brave-eai/dexjoco](https://github.com/brave-eai/dexjoco). Training/conversion code: [stpark7/dice-rl](https://github.com/stpark7/dice-rl), derived from [DICE-RL](https://github.com/real-stanford/dice-rl). The upstream raw dataset is published under the MIT license. The upstream copyright and license notice are included in [LICENSE](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](https://arxiv.org/abs/2605.16257). - [From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning](https://arxiv.org/abs/2603.10263).