時間依存ゴールを用いた強化学習によるロボット演奏家
Reinforcement Learning with Time-dependent Goals for Robotic Musicians
ゴール条件付き強化学習に時間次元を拡張した「時間依存ゴール」を提案し、テルミン演奏ロボットをシミュレーションで訓練して実機へ転移した。
著者: Thilo Fryen, Manfred Eppe, Phuong D. H. Nguyen, Timo Gerkmann, Stefan Wermter
分類: cs.RO, cs.AI
原文アブストラクト
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
