Towards the grand challenge of achieving human-level manipulation in robots, playing piano is a compelling testbed that requires strategic, precise, and flowing movements. Over the years, several works demonstrated hand-designed controllers on real world piano playing, while other works evaluated robot learning approaches on simulated piano scenarios. In this paper, we develop the first piano playing robotic system that makes use of learning approaches while also being deployed on a real world dexterous robot. Specifically, we make use of Sim2Real to train a policy in simulation using reinforcement learning before deploying the learned policy on a real world dexterous robot. In our experiments, we thoroughly evaluate the interplay between domain randomization and the accuracy of the dynamics model used in simulation. Moreover, we evaluate the robot's performance across multiple songs with varying complexity to study the generalization of our learned policy. By providing a proof-of-concept of learning to play piano in the real world, we want to encourage the community to adopt piano playing as a compelling benchmark towards human-level manipulation. We open-source our code and show additional videos at https://lasr.org/research/learning-to-play-piano .
Zeulner et al. (Wed,) studied this question.