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license: cc-by-4.0 |
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# Dataset Card for VIMA-Data |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Dataset Structure](#dataset-structure) |
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- [Dataset Creation](#dataset-creation) |
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- [Additional Information](#additional-information) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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## Dataset Description |
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- **Homepage:** https://vimalabs.github.io/ |
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- **Repository:** https://github.com/vimalabs/VimaBench |
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- **Paper:** https://arxiv.org/abs/2210.03094 |
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### Dataset Summary |
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This is the official dataset used to train general robot manipulation agents with multimodal prompts, as presented in [paper](https://arxiv.org/abs/2210.03094). It contains 650K trajectories for 13 tasks in [VIMA-Bench](https://github.com/vimalabs/VimaBench). All demonstrations are generated by oracles. |
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## Dataset Structure |
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Data are grouped into different tasks. Within each trajectory's folder, there are two folders `rgb_front` and `rgb_top`, and three files `obs.pkl`, `action.pkl`, and `trajectory.pkl`. RGB frames from a certain perspective are separately stored in corresponding folder. `obs.pkl` includes segmentation and state of end effector. `action.pkl` contains oracle actions. `trajectory.pkl` contains meta information such as elapsed steps, task information, and object information. Users can build their custom data piepline starting from here. More details and examples can be found [here](https://github.com/vimalabs/VimaBench#training-data). |
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## Dataset Creation |
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All demonstrations are generated by scripted oracles. |
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## Additional Information |
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### Licensing Information |
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This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode) license. |
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### Citation Information |
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If you find our work useful, please consider citing us! |
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```bibtex |
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@inproceedings{jiang2023vima, |
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title = {VIMA: General Robot Manipulation with Multimodal Prompts}, |
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author = {Yunfan Jiang and Agrim Gupta and Zichen Zhang and Guanzhi Wang and Yongqiang Dou and Yanjun Chen and Li Fei-Fei and Anima Anandkumar and Yuke Zhu and Linxi Fan}, |
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booktitle = {Fortieth International Conference on Machine Learning}, |
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year = {2023} |
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} |
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``` |
