nvidia/PhysicalAI-Robotics-Open-H-Embodiment

As of 3/16 the last components of the Open-H-Embodiment dataset are still being uploaded

Dataset Description:

Open-H-Embodiment is a community‑driven dataset initiative building the open, shared foundation needed to train and evaluate AI autonomy models for surgical robotics and ultrasound.

This dataset is a multi-embodiment collection of LeRobot datasets of paired kinematics and video, across tasks such as tabletop exercises, clinical procedures, as well as simulations of healthcare robotics applications.

Maintainer / Hosting Organization:

NVIDIA Corporation

Contributing Organizations:

Balgrist, CMR, CUHK, GBU (China), HKBU, Hamlyn, ImFusion, JHU, Leeds, MBZUAI (UAE), Moon Surgical, NVIDIA, Northwell, Obuda, PolyU (HK), Qilu Hospital of Shandong University, Rob Surgical, Sanoscience, SDSC, Semaphor, Stanford, TUD, TUM, Tuodao, Turin, UCB, UCSD, UIC, UTenn, UTexas, Vanderbilt, Virtual Incision

Dataset Stewardship:

Open-H-Embodiment community & Steering Group

Dataset Creation Date:

February 2026

License/Terms of Use:

CC-BY-4.0

Intended Usage:

Researchers and builders interested in training healthcare robotics autonomy models, world foundation models, or vision language models.

Dataset Characterization

** Data Collection Method

• Hybrid: Human, Automatic/Sensors, Synthetic

** Labeling Method

• Hybrid: Human, Automatic/Sensors, Synthetic

Dataset Format

LeRobot v2.1

Video stored as MP4 files per episode Kinematics stored as Parquet files. Metadata manifest as JSON/JSONL, linking each Parquet row to corresponding video.

Dataset Quantification

750 hours of training data. 120,000 trajectories of video paired with kinematics. 4.5 TB.

Reference(s):

https://github.com/open-h-embodiment/data-collection

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
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