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 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):

# 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.