Dynamic control strategy for robot music performance driven by reinforcement learning

Abstract

Music performance enables robots to show their emotions to humans through musical expression. Traditional systems face difficulties when they need to adapt to changing musical expressions and performance environments. The purpose of this research is to develop a dynamic control strategy for a violin-playing robotic system using Reinforcement learning (RL) to improve expressive performance. The proposed approach adapts key bowing dynamics, including bowing speed, bow pressure, bow direction, and timing, based on musical cues. An Entropy-regularized radial basis function with deep Q-network (ER-RBF-DQNet) model is introduced as the core framework. This hybrid RL architecture enhances nonlinear feature mapping and adaptive decision-making. It also improves exploration for more expressive robotic violin performance. A dataset of symbolic musical scores labeled with pitch, duration, and target sound pressure served as input. Normalization techniques were applied to scale musical features, and noise filtering was used to remove inconsistencies in dynamic annotations. Mel-frequency cepstral coefficients (MFCCs), along with tempo, pitch contour, and dynamic range, were extracted as expressive control parameters. The proposed method combines RBFN, DQN, and ER to map musical input features, approximate optimal Q-values, and encourage exploration during training. This allows the robot to generate control signals that align with the musical score, enhancing human-robot interaction through music. Python was implemented, and the evaluation, the accuracy (98.6%) indicates the proportion of robotic control operations that accurately matched the annotated ground-truth musical score. The proposed RL-based dynamic control system successfully transforms score elements into performance actions which enable robots to deliver expressive music performances.