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This model is fine-tuned across all tasks in our manipulation challenge, building upon our publicly released pretrained checkpoint.
Additional fine-tuning using the Agibot-World dataset is encouraged to further enhance performance.
| Model Name | Total Score | Clear the countertop waste | Open drawer and store items | Heat the food in the microwave | Pack moving objects from conveyor | Pickup items from the freezer | Restock supermarket items | Pack in the supermarket | Make a sandwich | Clear table in the restaurant | Stamp the seal |
|---|---|---|---|---|---|---|---|---|---|---|---|
| UniVLA | 2.336 | 0.194 | 0 | 0.198 | 0 | 0.08 | 0.55 | 1 | 0.064 | 0.25 | 0 |
1@article{bu2025univla,
2 title={UniVLA: Learning to Act Anywhere with Task-centric Latent Actions},
3 author={Qingwen Bu and Yanting Yang and Jisong Cai and Shenyuan Gao and Guanghui Ren and Maoqing Yao and Ping Luo and Hongyang Li},
4 journal={arXiv preprint arXiv:2505.06111},
5 year={2025}
6}1@article{bu2025agibot,
2 title={Agibot world colosseo: A large-scale manipulation platform for scalable and intelligent embodied systems},
3 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},
4 journal={arXiv preprint arXiv:2503.06669},
5 year={2025}
6}