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 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.
[2606.23848] Enforcing Human-like Kinematics in Dexterous Piano Playing via Adversarial Posture Regularization
[Submitted on 22 Jun 2026]
Title:Enforcing Human-like Kinematics in Dexterous Piano Playing via Adversarial Posture Regularization
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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 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: this https URL.
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Robotics (cs.RO)
Cite as: arXiv:2606.23848 [cs.RO]
(or arXiv:2606.23848v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.23848
arXiv-issued DOI via DataCite (pending registration)
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From: Bin Qiu [view email] [v1] Mon, 22 Jun 2026 18:30:36 UTC (1,193 KB)
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