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arXiv · 2410.13126

ALOHA Unleashed: A Simple Recipe for Robot Dexterity

Tony Z. Zhao·Jonathan Tompson·Danny Driess·Pete Florence·Kamyar Ghasemipour·Chelsea Finn·Ayzaan Wahid
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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. The project website and videos can be found at aloha-unleashed.github.io.

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Tony Z. Zhao, Jonathan Tompson, Danny Driess, Pete Florence, Kamyar Ghasemipour, Chelsea Finn, Ayzaan Wahid. 2024-10-17. ALOHA Unleashed: A Simple Recipe for Robot Dexterity. https://arxiv.org/abs/2410.13126

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