Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

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Abstract

ALOHA : A Low-cost Open-source Hardware System for Bimanual Teleoperation.The whole system costs <$20k with off-the-shelf robots and 3D printed components.Left: The user teleoperates by backdriving the leader robots, with the follower robots mirroring the motion.Right: ALOHA is capable of precise, contact-rich, and dynamic tasks.We show examples of both teleoperated and learned skills.Abstract-Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and closed-loop visual feedback.Performing these tasks typically requires high-end robots, accurate sensors, or careful calibration, which can be expensive and difficult to set up.Can learning enable low-cost and imprecise hardware to perform these fine manipulation tasks?We present a low-cost system that performs end-to-end imitation learning directly from real demonstrations, collected with a custom teleoperation interface.Imitation learning, however, presents its own challenges, particularly in high-precision domains: errors in the policy can compound over time, and human demonstrations can be non-stationary.To address these challenges, we develop a simple yet novel algorithm, Action Chunking with Transformers (ACT), which learns a generative model over action sequences.ACT allows the robot to learn 6 difficult tasks in the real world, such as opening a translucent condiment cup and slotting a battery with 80-90% success, with only 10 minutes worth of demonstrations.Project website: tonyzhaozh.github.io/aloha

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