Enforcing Human-like Kinematics in Dexterous Piano Playing via Adversarial Posture Regularization

arXiv:2606.23848v1 Announce Type: new Abstract: 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 matchi...