Datasetpublic

nv-njb/OpenXVQA

OpenXVQA VQA benchmark derived from Open X-Embodiment robot manipulation data. Tests visual understanding of diverse robotic scenes and actions. Format Each row contains: { "id": "<unique id>", "question": "<question text>", "choices": "['option1', 'option2', ...]", "correct_answer": <int index>, "image": <PNG bytes> } choices is a Python-literal string of a list of answer options. correct_answer is the integer index into choices of the correct option.… See the full description on the dataset page: https://huggingface.co/datasets/nv-njb/OpenXVQA.

sourceHugging Faceotherupdated 5mo agoView on Hugging Face

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Dataset Card

OpenXVQA

VQA benchmark derived from Open X-Embodiment robot manipulation data. Tests visual understanding of diverse robotic scenes and actions.

Format

Each row contains:

json

{
  "id": "<unique id>",
  "question": "<question text>",
  "choices": "['option1', 'option2', ...]",
  "correct_answer": <int index>,
  "image": <PNG bytes>
}

choices is a Python-literal string of a list of answer options. correct_answer is the integer index into choices of the correct option. image is embedded as binary in the parquet (HF Image() decodes on load).

Citation

@article{padalkar2023open,
  title={Open X-Embodiment: Robotic Learning Datasets and RT-X Models},
  author={Padalkar, Abhishek and ...},
  journal={arXiv:2310.08864},
  year={2023}
}

License

Annotations are derived from Open X-Embodiment, which is openly published. The MCQ formatting here is a research-only artifact.