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Learning to Use Chopsticks in Diverse Gripping Styles
Research & Education
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DescriptionWe propose a physics-based learning and control framework for using chopsticks. Robust hand controls for multiple hand morphologies and holding positions are first learned through Bayesian optimization and deep reinforcement learning. For tasks such as object relocation, the low-level controllers track collision-free trajectories synthesized by a high-level motion planner.