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arXiv · 2304.04150

RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning

Kevin Zakka·Philipp Wu·Laura Smith·Nimrod Gileadi·Taylor Howell·Xue Bin Peng·Sumeet Singh·Yuval Tassa·Pete Florence·Andy Zeng·Pieter Abbeel
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Abstract

Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics. Reinforcement learning is a promising approach that has achieved impressive progress in the last few years; however, the class of problems it has typically addressed corresponds to a rather narrow definition of dexterity as compared to human capabilities. To address this gap, we investigate piano-playing, a skill that challenges even the human limits of dexterity, as a means to test high-dimensional control, and which requires high spatial and temporal precision, and complex finger coordination and planning. We introduce RoboPianist, a system that enables simulated anthropomorphic hands to learn an extensive repertoire of 150 piano pieces where traditional model-based optimization struggles. We additionally introduce an open-sourced environment, benchmark of tasks, interpretable evaluation metrics, and open challenges for future study. Our website featuring videos, code, and datasets is available at https://kzakka.com/robopianist/

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Kevin Zakka, Philipp Wu, Laura Smith, Nimrod Gileadi, Taylor Howell, Xue Bin Peng, Sumeet Singh, Yuval Tassa, Pete Florence, Andy Zeng, Pieter Abbeel. 2023-04-09. RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning. https://arxiv.org/abs/2304.04150

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