Pluck and Play: Self-supervised Exploration of Chordophones for Robotic Playing

Existing robotic musicians utilize detailed handcrafted instrument models to generate or learn policies for playing because model-free or inaccurate policy rollouts might easily damage or wear out fragile instruments. We introduce an approach to characterize geometric models of chordophones and their audio onset responses directly through audio-tactile exploration with a physical robot arm. Initially, the system refines prior estimates of string positions, provided by kinesthetic teaching or visual estimation, through repeated attempts to pluck individual strings. A subsequent stage implements a Safe Active Exploration paradigm based on Gaussian Processes to explore and characterize the audio onset response of feasible plucking motions while minimizing invalid attempts. The resulting models can be used to actuate an imprecise robotic arm to play sequences of notes with varying loudness on a Chinese Guzheng.