agibot-world/GO-1-Air

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}
}