Robot musicians require precise control to obtain proper note accuracy, sound quality, and musical expression. Performance of string instruments, such as violin and cello, presents a significant challenge due to the precise control required over bow angle and pressure to produce the desired sound. While prior robotic cellists focus on accurate bowing trajectories, these works often rely on expensive motion capture techniques, and fail to sightread music in a human-like way. We propose a novel end-to-end MIDI score to robotic motion pipeline which converts musical input directly into collision-aware bowing motions for a UR5e robot cellist. Through use of Universal Robot Freedrive feature, our robotic musician can achieve human-like sound without the need for motion capture. Additionally, this work records live joint data via Real-Time Data Exchange (RTDE) as the robot plays, providing labeled robotic playing data from a collection of five standard pieces to the research community. To demonstrate the effectiveness of our method in comparison to human performers, we introduce the Musical Turing Test, in which a collection of 132 human participants evaluate our robot's performance against a human baseline. Human reference recordings are also released, enabling direct comparison for future studies. This evaluation technique establishes the first benchmark for robotic cello performance. Finally, we outline a residual reinforcement learning methodology to improve upon baseline robotic controls, highlighting future opportunities for improved string-crossing efficiency and sound quality.

翻译：机器人演奏家需要精确的控制以实现准确的音符、良好的音质和丰富的音乐表现力。演奏小提琴、大提琴等弦乐器尤其具有挑战性，因为这需要对琴弓的角度和压力进行精细调控才能产生理想的音色。尽管现有的机器人演奏系统专注于生成精确的运弓轨迹，但这些方法通常依赖昂贵的动作捕捉技术，且无法像人类演奏者一样实现视奏。本文提出了一种新颖的端到端MIDI乐谱至机器人动作生成流程，能够将音乐输入直接转换为UR5e机器人演奏大提琴时具有碰撞感知的运弓动作。通过利用Universal Robot的Freedrive功能，我们的机器人演奏家无需动作捕捉即可实现类人的演奏音色。此外，本研究通过实时数据交换（RTDE）记录机器人演奏过程中的实时关节数据，并向研究社区提供了包含五首标准曲目的标注机器人演奏数据集。为验证本方法相较于人类演奏者的有效性，我们引入了音乐图灵测试，邀请132位参与者将机器人演奏与人类基准演奏进行对比评估。同时公开了人类参考录音，为后续研究提供直接比较基准。该评估技术首次建立了机器人演奏大提琴的性能基准。最后，我们提出了一种基于残差强化学习的方法来改进基线机器人控制策略，为未来提升换弦效率和音质指明了改进方向。