发表机构
Zhejiang University; ZJU-Hangzhou Global Scientific and Technological Innovation Center（浙江大学; 浙江大学杭州国际科创中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于强化学习和图优化的机器人钢琴控制框架，结合物理声学模型，实现高保真且富有表现力的演奏，显著优于基线并接近人类水平。
AI 中文摘要
让机器人以人类水平的表达力演奏乐器，是弥合机械精度与艺术诠释之间鸿沟的前沿领域。尽管机器人灵巧性已取得进展，但复现人类钢琴家特有的流畅手指过渡和细腻动态控制仍然是一项重大挑战。通过基于强化学习的控制框架，我们证明了一只灵巧的机器人手能够在多样化的钢琴曲目中实现高保真演奏。我们方法的核心是一种基于图的优化策略，该策略引导机器人生成与人类运动模式高度相似的自然预压和按键指法策略。为了实现富有表现力的声音生成，控制系统与一个物理启发的声学模型相结合，该模型调节按键速度，以准确再现乐谱中指定的动态变化。定量评估表明，我们的表现力控制模型在手指形态相似性和动态速度准确性方面均显著优于基线方法。在一项涉及不同听众群体的感知测试中，我们系统生成的演奏显著优于基线机器人演奏，并且对于非专业听众而言，与人类演奏难以区分。此外，跨多种音乐风格的广泛实验证实，我们的方法在实现表现力演奏的同时保持了高音符级准确性。我们的方法为机器人系统超越单纯的机械精度提供了一条稳健的途径，将机器人音乐性提升至与人类钢琴家相当的表现力演奏水平。
英文摘要
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.
