{"ID":2846200,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.02504","arxiv_id":"2511.02504","title":"Dexterous Robotic Piano Playing at Scale","abstract":"Endowing robot hands with human-level dexterity has been a long-standing goal in robotics. Bimanual robotic piano playing represents a particularly challenging task: it is high-dimensional, contact-rich, and requires fast, precise control. We present OmniPianist, the first agent capable of performing nearly one thousand music pieces via scalable, human-demonstration-free learning. Our approach is built on three core components. First, we introduce an automatic fingering strategy based on Optimal Transport (OT), allowing the agent to autonomously discover efficient piano-playing strategies from scratch without demonstrations. Second, we conduct large-scale Reinforcement Learning (RL) by training more than 2,000 agents, each specialized in distinct music pieces, and aggregate their experience into a dataset named RP1M++, consisting of over one million trajectories for robotic piano playing. Finally, we employ a Flow Matching Transformer to leverage RP1M++ through large-scale imitation learning, resulting in the OmniPianist agent capable of performing a wide range of musical pieces. Extensive experiments and ablation studies highlight the effectiveness and scalability of our approach, advancing dexterous robotic piano playing at scale.","short_abstract":"Endowing robot hands with human-level dexterity has been a long-standing goal in robotics. Bimanual robotic piano playing represents a particularly challenging task: it is high-dimensional, contact-rich, and requires fast, precise control. We present OmniPianist, the first agent capable of performing nearly one thousan...","url_abs":"https://arxiv.org/abs/2511.02504","url_pdf":"https://arxiv.org/pdf/2511.02504v1","authors":"[\"Le Chen\",\"Yi Zhao\",\"Jan Schneider\",\"Quankai Gao\",\"Simon Guist\",\"Cheng Qian\",\"Juho Kannala\",\"Bernhard Schölkopf\",\"Joni Pajarinen\",\"Dieter Büchler\"]","published":"2025-11-04T11:46:02Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Reinforcement Learning\",\"Transformer\"]","has_code":false}