HandelBot: Real-World Piano Playing via Fast Adaptation of Dexterous Robot Policies

• URL: http://arxiv.org/abs/2603.12243v2
• Date: Sun, 15 Mar 2026 17:16:45 GMT
• Title: HandelBot: Real-World Piano Playing via Fast Adaptation of Dexterous Robot Policies
• Abstract summary: HandelBot is a framework that combines a simulation policy and rapid adaptation through a two-stage pipeline.<n>We demonstrate that HandelBot can successfully perform precise bimanual piano playing.<n>Our system outperforms direct simulation deployment by a factor of 1.8x and requires only 30 minutes of physical interaction data.
• Score: 32.80504141722999
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mastering dexterous manipulation with multi-fingered hands has been a grand challenge in robotics for decades. Despite its potential, the difficulty of collecting high-quality data remains a primary bottleneck for high-precision tasks. While reinforcement learning and simulation-to-real-world transfer offer a promising alternative, the transferred policies often fail for tasks demanding millimeter-scale precision, such as bimanual piano playing. In this work, we introduce HandelBot, a framework that combines a simulation policy and rapid adaptation through a two-stage pipeline. Starting from a simulation-trained policy, we first apply a structured refinement stage to correct spatial alignments by adjusting lateral finger joints based on physical rollouts. Next, we use residual reinforcement learning to autonomously learn fine-grained corrective actions. Through extensive hardware experiments across five recognized songs, we demonstrate that HandelBot can successfully perform precise bimanual piano playing. Our system outperforms direct simulation deployment by a factor of 1.8x and requires only 30 minutes of physical interaction data.

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