GO-1 Air
GO-1 is our robotic foundation model pretrained on AgiBot World Dataset. GO-1 Air is GO-1 model without Latent Planner, which is high-performanced and lightweighted.
Please refer to our project page, github repo and paper for more details.
Model Details
Model Description
- Developed by: Team AgiBot-World
- Model type: Vision-Language-Action model
- License: CC BY-NC-SA 4.0
- Vision Language Model: InternVL 2.5-2B
- Pre-training Dataset: AgiBot World Dataset
Model Sources
- Repository: https://github.com/OpenDriveLab/Agibot-World
- Paper: https://arxiv.org/abs/2503.06669
- Project Page: https://agibot-world.com/
Uses
This is the pre-trained GO-1 Air model. For fine-tuning on simulation benchmarks or your customized dataset, please visit our github repo.
Citation
- Please consider citing our work if it helps your research.
- For the full authorship and detailed contributions, please refer to contributions.
- In alphabetical order by surname:
@article{bu2025agibot,
title={Agibot world colosseo: A large-scale manipulation platform for scalable and intelligent embodied systems},
author={Bu, Qingwen and Cai, Jisong and Chen, Li and Cui, Xiuqi and Ding, Yan and Feng, Siyuan and Gao, Shenyuan and He, Xindong and Huang, Xu and Jiang, Shu and others},
journal={arXiv preprint arXiv:2503.06669},
year={2025}
}
@inproceedings{bu2025agibot,
title={Agibot world colosseo: A large-scale manipulation platform for scalable and intelligent embodied systems},
author={Bu, Qingwen and Cai, Jisong and Chen, Li and Cui, Xiuqi and Ding, Yan and Feng, Siyuan and He, Xindong and Huang, Xu and others},
booktitle={2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year={2025},
organization={IEEE}
}