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

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[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 View a PDF of the paper titled Enforcing Human-like Kinematics in Dexterous Piano Playing via Adversarial Posture Regularization, by Bin Qiu and 3 other authors View PDF HTML (experimental) 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. Subjects: 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) Submission history From: Bin Qiu [view email] [v1] Mon, 22 Jun 2026 18:30:36 UTC (1,193 KB) Full-text links: Access Paper: View a PDF of the paper titled Enforcing Human-like Kinematics in Dexterous Piano Playing via Adversarial Posture Regularization, by Bin Qiu and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)