Reinforcement learning is a promising method to accomplish robotic control tasks. The task of playing musical instruments is, however, largely unexplored because it involves the challenge of achieving sequential goals - melodies - that have a temporal dimension. In this paper, we address robotic musicianship by introducing a temporal extension to goal-conditioned reinforcement learning: Time-dependent goals. We demonstrate that these can be used to train a robotic musician to play the theremin instrument. We train the robotic agent in simulation and transfer the acquired policy to a real-world robotic thereminist. Supplemental video: https://youtu.be/jvC9mPzdQN4
@article{arxiv.2011.05715,
title = {Reinforcement Learning with Time-dependent Goals for Robotic Musicians},
author = {Thilo Fryen and Manfred Eppe and Phuong D. H. Nguyen and Timo Gerkmann and Stefan Wermter},
journal= {arXiv preprint arXiv:2011.05715},
year = {2020}
}
Comments
Preprint, submitted to IEEE Robotics and Automation Letters (RA-L) 2021 with International Conference on Robotics and Automation Conference Option (ICRA) 2021