ALOHA Unleashed: A Simple Recipe for Robot Dexterity
Abstract
Recent work has shown promising results for learning end-to-end robot policies using imitation learning. In this work we address the question of how far can we push imitation learning for challenging dexterous manipulation tasks. We show that a simple recipe of large scale data collection on the ALOHA 2 platform, combined with expressive models such as Diffusion Policies, can be effective in learning challenging bimanual manipulation tasks involving deformable objects and complex contact rich dynamics. We demonstrate our recipe on 5 challenging real-world and 3 simulated tasks and demonstrate improved performance over state-of-the-art baselines.
Cite
Text
Zhao et al. "ALOHA Unleashed: A Simple Recipe for Robot Dexterity." Proceedings of The 8th Conference on Robot Learning, 2024.Markdown
[Zhao et al. "ALOHA Unleashed: A Simple Recipe for Robot Dexterity." Proceedings of The 8th Conference on Robot Learning, 2024.](https://mlanthology.org/corl/2024/zhao2024corl-aloha/)BibTeX
@inproceedings{zhao2024corl-aloha,
title = {{ALOHA Unleashed: A Simple Recipe for Robot Dexterity}},
author = {Zhao, Tony Z. and Tompson, Jonathan and Driess, Danny and Florence, Pete and Ghasemipour, Seyed Kamyar Seyed and Finn, Chelsea and Wahid, Ayzaan},
booktitle = {Proceedings of The 8th Conference on Robot Learning},
year = {2024},
pages = {1910-1924},
volume = {270},
url = {https://mlanthology.org/corl/2024/zhao2024corl-aloha/}
}