Authors: Yi Zhao, Le Chen, Jan Schneider, Quankai Gao, Juho Kannala, Bernhard Schölkopf, Joni Pajarinen, Dieter Büchler
Abstract: It has been a long-standing research goal to endow robot hands with
human-level dexterity. Bi-manual robot piano playing constitutes a task that
combines challenges from dynamic tasks, such as generating fast while precise
motions, with slower but contact-rich manipulation problems. Although
reinforcement learning based approaches have shown promising results in
single-task performance, these methods struggle in a multi-song setting. Our
work aims to close this gap and, thereby, enable imitation learning approaches
for robot piano playing at scale. To this end, we introduce the Robot Piano 1
Million (RP1M) dataset, containing bi-manual robot piano playing motion data of
more than one million trajectories. We formulate finger placements as an
optimal transport problem, thus, enabling automatic annotation of vast amounts
of unlabeled songs. Benchmarking existing imitation learning approaches shows
that such approaches reach state-of-the-art robot piano playing performance by
leveraging RP1M.
Source: http://arxiv.org/abs/2408.11048v1
