Research on the Practice and Application Path of Bionic Robots in the Field of Piano Performance

Abstract

Traditional bionic piano-playing robots have long been confined to the fixed paradigm of "score reading - mechanical reproduction," suffering from three limitations: single perception method, scarce training data, and lack of creative and performing capabilities. This leads to the convergence of product functionalities, the flattening of user experience, and the difficulty of establishing effective market competitiveness. To address these issues, this paper has constructed an integrated intelligent music performance system of "perception - training - creation". At the perception layer, multimodal recognition combining acoustic recognition and visual recognition is introduced to achieve the mutual understanding and verification of score images and audio; at the data layer, passive mechanical exoskeletons are used to collect data such as joint angles and fingertip force of professional performers, and a force-emotion mapping dictionary and a spatiotemporal probability model for hand coordination are established; at the creation and performance layer, an artificial intelligence music creation module is designed, generating adapted scores that can be smoothly performed by the mechanical hand through style transfer and constraint optimization. Experimental verification shows that the accuracy of multi-modal score recognition has significantly improved compared to single visual recognition, the coordinated training data collected by the exoskeleton reduces the error in mechanical hand performance, and combined with the artificial intelligence composition module, it can stably perform multi-style adapted pieces. The core innovation of this paper lies in integrating the complete data loop from score recognition to performance, upgrading the bionic piano-playing robot from a fixed-score playback terminal to an intelligent performing entity with understanding, execution capabilities, and secondary creation. The research results provide a technical framework and a practical path for personalized design schemes and commercialization of intelligent music education.