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

Keywords: Imitation Learning, Manipulation

TL;DR: A simple recipe for dexterous autonomous policies with scalable data collection and Diffusion Policies

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.

Website: https://aloha-unleashed.github.io

Student Paper: no

Submission Number: 447