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OpenNeoData

OpenNeoData is a large-scale real-world robot manipulation dataset: 200k+ trajectories / 5,000+ hours collected on 6 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 v3.0 format, one sub-dataset per embodiment, dual-hosted on Hugging Face and ModelScope.

Key Features 🔑

200k+ trajectories from 6 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, 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):

# 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:

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

or with git sparse-checkout:

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):

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.

# 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 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.

@article{n0foundation2026,
  title   = {N0-Foundation: Towards the Age of Tactile Intelligence},
  author  = {NeoteAI Team and Fudan TEAI Team},
  journal = {Technical Report},
  year    = {2026}
}

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