README.md

---
license: cc-by-nc-sa-4.0
task_categories:
  - robotics
tags:
  - robotics
  - robot-manipulation
  - imitation-learning
  - embodied-ai
  - lerobot
  - real-world
  - dual-arm
  - tactile-sensing
pretty_name: OpenNeoData
size_categories:
  - "100K<n<1M"
language:
  - en
---

# OpenNeoData

[![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
[![LeRobot](https://img.shields.io/badge/LeRobot-v3.0-blue)](https://github.com/huggingface/lerobot)
[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-OpenNeoData-yellow)](https://huggingface.co/datasets/XinzhiEmbodied/OpenNeoData)
[![ModelScope](https://img.shields.io/badge/ModelScope-OpenNeoData-624AFF)](https://modelscope.cn/datasets/NeoteAI/OpenNeoData)
[![Trajectories](https://img.shields.io/badge/Trajectories-200k-brightgreen)](https://huggingface.co/datasets/XinzhiEmbodied/OpenNeoData)

OpenNeoData is a large-scale real-world robot manipulation dataset: **200k+ trajectories / 5,000+ hours** collected on **7 embodiments** — five fixed-arm robot platforms and two handheld UMI device families — with **wrist-mounted visuotactile sensing on every embodiment**. All data is released in [LeRobot](https://github.com/huggingface/lerobot) **v3.0** format, one sub-dataset per embodiment, dual-hosted on Hugging Face and ModelScope.

## Key Features 🔑

- **200k+ trajectories** from 7 embodiments, with a total duration of **5,000+ hours**.
- **Tactile-complete**: every gripper carries two gel visuotactile cameras, time-aligned with RGB at 30 fps across the entire dataset.
- **Diverse embodiments**: dual-arm ALOHA and ARX-5, single-arm ARX-5, Flexiv Rizon 4s, UR5e/UR7e, and dual-/single-hand UMI devices.
- **257 task types** with descriptions, covering contact-rich manipulation, deformable objects, precise placement and bimanual coordination.

## Get started 🔥

### Download the Dataset

Hugging Face ([`NeoteAIEmbodied/OpenNeoData`](https://huggingface.co/datasets/NeoteAIEmbodied/OpenNeoData)):

```bash
# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install

git clone https://huggingface.co/datasets/NeoteAIEmbodied/OpenNeoData

# If you want to clone without large files - just their pointers
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/NeoteAIEmbodied/OpenNeoData
```

The dataset is organized as **one folder per embodiment**, so you can download a single robot instead of all 5,000 hours. For example, only `ur`:

```bash
pip install -U huggingface_hub
hf download NeoteAIEmbodied/OpenNeoData --repo-type dataset \
  --include "ur/**" --local-dir OpenNeoData
```

or with git sparse-checkout:

```bash
git lfs install
git init OpenNeoData && cd OpenNeoData
git remote add origin https://huggingface.co/datasets/NeoteAIEmbodied/OpenNeoData
git sparse-checkout init
git sparse-checkout set ur
git pull origin main
```

ModelScope mirror ([`NeoteAI/OpenNeoData`](https://modelscope.cn/datasets/NeoteAI/OpenNeoData)):

```bash
pip install modelscope
modelscope download --dataset NeoteAI/OpenNeoData --include "ur/**" --local_dir OpenNeoData
```

### Load with LeRobot

Each embodiment folder is a standalone LeRobot **v3.0** dataset. Our project relies solely on the `lerobot` library — please follow their [installation instructions](https://github.com/huggingface/lerobot).

```python
# pip install "lerobot>=0.6"
from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("local/openneodata_ur", root="OpenNeoData/ur")
item = ds[0]   # observation.state / action / observation.images.* ...
```

We would like to express our gratitude to the developers of [lerobot](https://github.com/huggingface/lerobot) for their outstanding contributions to the open-source community.

## Dataset Structure

### Folder hierarchy

```
OpenNeoData
├── aloha            # one LeRobot v3.0 dataset per embodiment
│   ├── meta
│   │   ├── info.json                          # fps, features, shapes, chunking
│   │   ├── tasks.parquet                      # task_index -> language instruction
│   │   ├── episodes/chunk-000/file-000.parquet# per-episode index, lengths, offsets
│   │   └── stats.json                         # per-feature normalization stats
│   ├── data
│   │   └── chunk-000
│   │       ├── file-000.parquet               # states/actions, many episodes per file
│   │       └── ...
│   └── videos
│       ├── observation.images.third_view
│       │   └── chunk-000
│       │       ├── file-000.mp4               # ~500 MB, many episodes per file
│       │       └── ...
│       ├── observation.images.left_wrist_view
│       ├── observation.images.left_wrist_left_tactile
│       ├── observation.images.left_wrist_right_tactile
│       └── ...                                # per-embodiment key set, see below
├── arx5
├── arx5_single
├── flexiv
├── ur
├── umi
└── umi_single
```

Following LeRobot v3.0, episodes are **aggregated** into ~500 MB parquet/MP4 files (`chunks_size=1000` files per `chunk-XXX` directory); each episode lies entirely within a single file, and `meta/episodes` maps every episode to its file and time offsets. The loader resolves all of this transparently.

### Embodiments and camera streams

| folder | robot | arms | video streams per frame |
|---|---|---|---|
| `aloha` | ALOHA (dual) | 2 | 7 = third + 2 wrist + 4 tactile |
| `arx5` | ARX-5 (dual) | 2 | 7 = third + 2 wrist + 4 tactile |
| `arx5_single` | ARX-5 (single-arm) | 1 | 4 = third + wrist + 2 tactile |
| `flexiv` | Flexiv Rizon 4s (7-DoF) | 1 | 4 = third + wrist + 2 tactile |
| `ur` | UR5e / UR7e | 1 | 4 = third + wrist + 2 tactile |
| `umi` | UMI (dual-hand) | 2 | 6 = 2 wrist + 4 tactile |
| `umi_single` | UMI (single-hand) | 1 | 3 = wrist + 2 tactile |

Camera keys follow the pattern `observation.images.third_view`, `observation.images.{left,right}_wrist_view` and `observation.images.{left,right}_wrist_{left,right}_tactile`; `*_tactile` are gel-pad camera streams from the visuotactile sensors. All streams are HEVC (`hvc1`) MP4, 640×360, `yuv420p`, 30 fps, max keyframe interval 10 frames.

### Feature keys

| key | fixed-arm robots | UMI |
|---|---|---|
| `observation.state` | measured joint positions + gripper | raw tracked EEF state |
| `action` | commanded joint positions + gripper | raw tracked EEF action |
| `observation.eef_pose` | measured EEF pose | — (state is already EEF) |
| `action.eef_pose` | commanded EEF pose | — |
| `observation.images.*` | 30 fps video streams | 30 fps video streams |

Per-embodiment shapes:

| folder | `state` / `action` | `eef_pose` |
|---|---|---|
| `aloha` | 14 (2 × 7) | 20 (2 × 10) |
| `arx5` | 14 (2 × 7) | 20 (2 × 10) |
| `arx5_single` | 7 | 10 |
| `flexiv` | 8 (7 joints + gripper) | 10 |
| `ur` | 7 (6 joints + gripper) | 10 |
| `umi` | 20 (2 × 10, raw EEF) | — |
| `umi_single` | 10 (raw EEF) | — |

Each 10-dim EEF block is `[x, y, z, rot6d(6), gripper]` per arm, left arm first. **rot6d** is the first two columns of the rotation matrix flattened column-major: `[R00, R10, R20, R01, R11, R21]`.

**UMI note**: the handheld UMI devices have no joint encoders, so `observation.state` / `action` directly carry the raw tracked end-effector trajectory (position + rot6d + gripper per hand); no separate `*.eef_pose` keys are published for `umi` / `umi_single`.

## License and Citation

All the data and code within this repo are under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).

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