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ALOHA Unleashed: A Simple Recipe for Robot Dexterity

aloha-unleashed.github.io · 448 words · saved by 1 readers

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. We introduce ALOHA Unleashed, a general imitation learning system for training dexterous policies on robots. We demonstrate results on ALOHA 2, which consists of a bimanual parallel-jaw gripper workcell with two 6-DoF arms. ALOHA Unleashed consists of a framework for scalable teleoperation that allows users to collect data to teach robots, comb

ALOHA Unleashed: A Simple Recipe for Robot Dexterity --> ALOHA Unleashed 🌋: A Simple Recipe for Robot Dexterity Tony Z. Zhao* Jonathan Tompson Danny Driess Pete Florence Kamyar Ghasemipour Chelsea Finn Ayzaan Wahid* * denotes equal contribution. Paper 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 expressiv

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