Blame for README.md

---
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 <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`.

```text
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](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).