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---
license: cc-by-4.0
---
# Dataset Card for VIMA-Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://vimalabs.github.io/
- **Repository:** https://github.com/vimalabs/VimaBench
- **Paper:** https://arxiv.org/abs/2210.03094
### Dataset Summary
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.
## Dataset Structure
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).
## Dataset Creation
All demonstrations are generated by scripted oracles.
## Additional Information
### Licensing Information
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.
### Citation Information
If you find our work useful, please consider citing us!
```bibtex
@inproceedings{jiang2023vima,
title = {VIMA: General Robot Manipulation with Multimodal Prompts},
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},
booktitle = {Fortieth International Conference on Machine Learning},
year = {2023}
}
``` |