用强化学习让机器人学会打鼓，能自动生成人类般的鼓点节奏。
Robot Drummer: Learning Rhythmic Skills for Humanoid Drumming
- 将打鼓分解为时间触发的接触事件链，分段训练单一策略
- 在30多首歌曲上实现高精度节奏识别（高F1值）
- 可生成跨臂击打、自适应换棒等类人打鼓动作，适合创意表演研究
类人机器人在灵巧性、平衡和行走方面取得显著进展，但在音乐表演等表达性领域仍处于探索阶段。打鼓等音乐任务对毫秒级时机、快速触碰及多肢体协调有极高要求，且持续时间可达数分钟。本文提出 Robot Drummer，一个面向类人打鼓的仿真框架，涵盖多样曲目。我们将类人打鼓建模为时序接触事件组成的韵律接触链（Rhythmic Contact Chain），通过将每首曲子拆分为固定长度片段，采用强化学习并行训练单一策略以应对长时程挑战。在超过三十首流行曲目的实验中，结果表明 Robot Drummer 能稳定达到高 F1 分数，实现高效长时程音乐表演学习。所学行为展现出跨臂击打、自适应鼓棒分配等类人打鼓策略，证明强化学习可推动类人机器人进入创造性音乐表演领域。项目页面：robotdrummer.github.io
原文摘要 · Abstract (English)
Humanoid robots have seen remarkable advances in dexterity, balance, and locomotion, yet their role in expressive domains such as music performance remains largely unexplored. Musical tasks, like drumming, present unique challenges such as split-second timing, rapid contacts, and multi-limb coordination over performances lasting minutes. In this paper, we introduce Robot Drummer, a simulation framework for humanoid drumming across a diverse repertoire of songs. We formulate humanoid drumming as the realization of timed contact events encoded as a Rhythmic Contact Chain. To handle the long-horizon nature of musical performance, we decompose each track into fixed-length segments and train a single policy across all segments in parallel using reinforcement learning. Through extensive experiments on over thirty popular tracks, our results demonstrate that Robot Drummer consistently achieves high F1 scores and enables efficient learning of long-horizon musical performances. The learned behaviors exhibit emergent human-like drumming strategies, such as cross-arm strikes, and adaptive stick assignments, demonstrating the potential of reinforcement learning to bring humanoid robots into the domain of creative musical performance. Project page: robotdrummer.github.io
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