RoboPianist：基于深度强化学习的灵巧钢琴演奏
RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning
AI总结：
针对机器人灵巧性不足的问题，提出RoboPianist系统，利用深度强化学习使模拟拟人手学习演奏150首钢琴曲，并开源环境、基准与评估指标。
摘要：
在机器人手中复现人类般的灵巧性是机器人学中最大的开放问题之一。强化学习是一种有前景的方法，在过去几年中取得了令人瞩目的进展；然而，与人类能力相比，它所通常处理的问题类别对应着一种相当狭窄的灵巧性定义。为弥补这一差距，我们研究钢琴演奏——一项甚至挑战人类灵巧性极限的技能——作为测试高维控制的手段，这需要高空间和时间精度以及复杂的手指协调与规划。我们引入了RoboPianist，一个使模拟的拟人化手能够学习150首钢琴曲目的系统，而传统的基于模型的优化在这些曲目上表现困难。我们还引入了一个开源环境、任务基准、可解释的评估指标以及未来研究的开放挑战。我们的网站包含视频、代码和数据集，可在 https://kzakka.com/robopianist/ 访问。
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
