---
dataset_info:
  features:
    - name: metadata
      dtype: string
configs:
  - config_name: default
    data_files:
      - split: train
        path: metadata/openh_dvrk_manifest.train.jsonl
license: cc-by-4.0
private: false
---
# Open-H dVRK convenience subset

This dataset is a convenience subset of
[`nvidia/PhysicalAI-Robotics-Open-H-Embodiment`](https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Open-H-Embodiment),
curated for dVRK/da Vinci surgical robotics experiments and staged so training
runs can stream from one place instead of rehydrating the upstream corpus
file-by-file.

The NVIDIA dataset is the canonical source: a CC-BY-4.0, LeRobot v2.1 corpus of
paired healthcare robotics video and kinematics, with video stored as MP4 and
kinematics stored as Parquet. All substantive credit for the source data belongs
with NVIDIA and the Open-H Embodiment contributors. Please cite and review the
upstream dataset card, licence and terms before using this snapshot. This
repository exists to make reproducible dVRK-domain Cosmos post-training less
theatrical.

Media and tensors are stored under `data/openh_snapshot/`; manifests and
snapshot metadata are under `metadata/`.

## Intended use

- Domain-specific post-training of action-conditioned video models.
- dVRK action-space validation and short-horizon surgical world-model research.
- Streaming/restart-friendly training on ephemeral GPU instances.

This snapshot is not a clinical dataset release, a new annotation layer or a
replacement for the upstream Open-H Embodiment corpus.

## Streaming the manifest

```python
from datasets import load_dataset

repo_id = "chrisvoncsefalvay/openembodiment-dvrk-subset"
manifest = load_dataset(
    "json",
    data_files=f"hf://datasets/{repo_id}/metadata/openh_dvrk_manifest.train.jsonl",
    split="train",
    streaming=True,
)
first = next(iter(manifest))
```

## Rehydrating the curated snapshot

```python
from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="chrisvoncsefalvay/openembodiment-dvrk-subset",
    repo_type="dataset",
    allow_patterns=["data/openh_snapshot/**", "metadata/**"],
)
```

The upload path uses Hugging Face Hub Xet storage when available, so resumed
uploads/downloads avoid the upstream API-rate-limit bottleneck.

## Citation

If you use this convenience subset, cite both this dataset repository and the
upstream Open-H Embodiment source corpus. This subset mirrors selected
dVRK/da Vinci roots from
[`nvidia/PhysicalAI-Robotics-Open-H-Embodiment`](https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Open-H-Embodiment);
all source data credit remains with Open-H and the original contributors.

```bibtex
@misc{voncsefalvay2026openembodimentdvrksubset,
  title = {Open-H dVRK convenience subset},
  author = {von Csefalvay, Chris},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/chrisvoncsefalvay/openembodiment-dvrk-subset},
  note = {Convenience dVRK/da Vinci subset of nvidia/PhysicalAI-Robotics-Open-H-Embodiment}
}

@misc{nvidia2026physicalairoboticsopenh,
  title = {PhysicalAI-Robotics-Open-H-Embodiment},
  author = {{NVIDIA Corporation} and {Open-H Embodiment community}},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Open-H-Embodiment},
  note = {CC-BY-4.0 Open-H Embodiment LeRobot v2.1 source corpus}
}

@misc{openh2026medicalrobotics,
  title = {Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics},
  author = {{Open-H-Embodiment Consortium}},
  year = {2026},
  eprint = {2604.21017},
  archivePrefix = {arXiv},
  primaryClass = {cs.RO},
  url = {https://arxiv.org/abs/2604.21017}
}
```