敵対的姿勢正則化による人間らしいキネマティクスの巧みなピアノ演奏への適用
Enforcing Human-like Kinematics in Dexterous Piano Playing via Adversarial Posture Regularization
強化学習でピアノを弾く高自由度ロボットハンドの不自然な姿勢を、少量の人間の演奏データを使った敵対的学習で人間らしく矯正する手法を提案した。
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著者: Bin Qiu, Yanming Shao, Guanyu Cai, Yao Mu
分類: cs.RO
原文アブストラクト
Reinforcement learning can train bimanual dexterous hands to play piano in physics simulation with high note accuracy, but for high-DoF dexterous hands, relying solely on task rewards or IK inversion often leads to unnatural postures and joint overextension. We propose \textit{Adversarial Posture Regularization (APR)}. It avoids expensive, song-aligned expert demonstration data and instead uses a small amount of casual human playing data. By matching the distribution of the posture of the policy with the human prior through an adversarial objective, APR encourages more human-like hand shapes. Meanwhile, we collect and release unstructured hand motion data of piano playing using a consumer-grade Meta Quest 3, and retarget the key motion information to the Shadow Hand. Finally, we achieve significantly better performance than prior methods on all three human-likeness metrics (cPSI, BSE, and FAC) as well as in visual quality. Project repository: https://github.com/APRProject/APRPianist.
