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マニピュレーションarXiv:2106.02040

器用な手と触覚でピアノを弾く学習への挑戦

Towards Learning to Play Piano with Dexterous Hands and Touch

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強化学習を用いて、楽譜から直接ピアノ演奏を器用な手で学習する手法を提案し、触覚を活用した報酬とカリキュラムでリズムや音量、運指の要求に対応できることを示した。

著者: Huazhe Xu, Yuping Luo, Shaoxiong Wang, Trevor Darrell, Roberto Calandra

分類: cs.RO, cs.AI, stat.ML

原文アブストラクト

The virtuoso plays the piano with passion, poetry and extraordinary technical ability. As Liszt said (a virtuoso)must call up scent and blossom, and breathe the breath of life. The strongest robots that can play a piano are based on a combination of specialized robot hands/piano and hardcoded planning algorithms. In contrast to that, in this paper, we demonstrate how an agent can learn directly from machine-readable music score to play the piano with dexterous hands on a simulated piano using reinforcement learning (RL) from scratch. We demonstrate the RL agents can not only find the correct key position but also deal with various rhythmic, volume and fingering, requirements. We achieve this by using a touch-augmented reward and a novel curriculum of tasks. We conclude by carefully studying the important aspects to enable such learning algorithms and that can potentially shed light on future research in this direction.

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