---
license: cc-by-4.0
language:
- en
pretty_name: Molmo2-ER robovqa
tags:
- embodied-reasoning
- molmo2
- molmo2-er
- vlm-training-data
---

# Molmo2-ER · Google DeepMind RoboVQA

Human-annotated long-horizon robotics video QA across three embodiments.

This is a re-hosted, **loader-ready subset** of the upstream dataset, used to train [`allenai/Molmo2-ER-4B`](https://huggingface.co/allenai/Molmo2-ER-4B). Files mirror the upstream layout; nothing in the data has been modified.

## Upstream source

- **Original dataset:** [Google DeepMind RoboVQA](https://robovqa.github.io)
- **Paper:** *RoboVQA: Multimodal Long-Horizon Reasoning for Robotics* ([arXiv:2311.00899](https://arxiv.org/abs/2311.00899))
- **License:** `cc-by-4.0` (inherits from upstream)

If you use this data, please cite the original authors:

```bibtex
@misc{sermanet2023robovqamultimodallonghorizonreasoning,
  title={RoboVQA: Multimodal Long-Horizon Reasoning for Robotics},
  author={Pierre Sermanet and Tianli Ding and Jeffrey Zhao and others},
  year={2023},
  eprint={2311.00899},
  archivePrefix={arXiv}
}
```

## Extracting before training

This release ships archives. Extract them in-place before pointing `SPATIAL_DATA_HOME` at this directory:

```bash
cat clips_extracted.tar.* > clips_extracted.tar && tar -xf clips_extracted.tar
```

## Usage in Molmo2-ER

See the [`allenai/molmo2`](https://github.com/allenai/molmo2) repository for the data loader and training recipe. The relevant loader class for this dataset lives in `olmo/data/spatial_datasets.py`.