| --- |
| 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`. |
|
|
