外观
论文arXiv 2609.108441 个月前
富有表现力的机器人钢琴家：用 Graph-Mimic 与音乐动态掌握复杂钢琴曲目
原文链接：Expressive Robotic Pianist: Mastering Complex Piano Repertoire with Graph-Mimic and Musical Dynamics（arXiv 2609.10844 · 发布日期：2026-09-09）
Expressive Robotic Pianist: Mastering Complex Piano Repertoire with Graph-Mimic and Musical Dynamics
该研究用强化学习、图优化和声学模型控制灵巧手，在多风格钢琴曲目上实现富有表现力的演奏。
核心要点
- 目标是弥合机械精度与艺术诠释差距，复现人类钢琴家的流畅指法与细腻力度。
- 方法采用强化学习控制框架，并以图优化生成接近人类运动模式的预按键和按键指法。
- 系统结合受物理启发的声学模型，调节按键速度以再现乐谱指定的动态变化。
- 定量评估和听感测试显示，该方法在指形相似性、速度准确性和听感偏好上优于基线。
验证范围与局限：验证覆盖多种音乐风格的钢琴曲目和听感测试，摘要未说明具体真机规模。
英文摘要 (Abstract)
Enabling robots to perform musical instruments with human-level expressivity represents a frontier in bridging the gap between mechanical precision and artistic interpretation. Despite advances in robotic dexterity, replicating the fluid finger transitions and nuanced dynamic control characteristic of human pianists remains a significant challenge. Through a reinforcement learning-based control framework, we demonstrate that a dexterous robotic hand can achieve high-fidelity performance across a diverse piano repertoire. Central to our approach is a graph-based optimization strategy that guides the robot to generate natural pre-press and key-press fingering strategies that closely resemble human movement patterns. To achieve expressive sound production, the control system is coupled with a physics-inspired acoustic model that modulates keypress velocity to accurately reproduce the dynamic variations specified in musical scores. Quantitative evaluations demonstrate that our expressive control model significantly outperforms baseline methods in both finger morphology similarity and dynamic velocity accuracy. In a perceptual test involving participants from diverse listener groups, performances generated by our system are significantly preferred over baseline robotic performances and are indistinguishable from human performances for non-professional audiences. Furthermore, extensive experiments across multiple musical styles confirm that our method maintains high note-level accuracy while achieving expressive performance. Our approach provides a robust pathway for robotic systems to move beyond mere mechanical accuracy, elevating robotic musicianship to a level of expressive performance comparable to human pianists.
作者：Yanhong Liang, Xianwei Liu, Chaojie Fu, Shaowen Cheng, Yuan, Yanyan, Chengwei Zhuo, Xi Chen, Yongbin Jin, Wei Yang, Hongtao Wang
来源
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