ALOHA Unleashed: A Simple Recipe for Robot Dexterity
Tony Z. Zhao, Jonathan Tompson, Danny Driess, Pete Florence, Seyed Kamyar Seyed Ghasemipour, Chelsea Finn, Ayzaan Wahid
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.
BibTeX
@inproceedings{
zhao2024aloha,
title={{ALOHA} Unleashed: A Simple Recipe for Robot Dexterity},
author={Tony Z. Zhao and Jonathan Tompson and Danny Driess and Pete Florence and Seyed Kamyar Seyed Ghasemipour and Chelsea Finn and Ayzaan Wahid},
booktitle={8th Annual Conference on Robot Learning},
year={2024},
url={https://openreview.net/forum?id=gvdXE7ikHI}
}