HandelBot：通过快速适应灵巧机器人策略实现真实世界钢琴演奏
HandelBot: Real-World Piano Playing via Fast Adaptation of Dexterous Robot Policies
AI总结：
本文提出HandelBot框架，结合仿真策略和快速适应，通过两阶段流程实现高精度双手钢琴演奏，实验表明其在真实环境中的表现优于直接仿真部署。
摘要：
掌握多指手的灵巧操作一直是机器人领域几十年来的重大挑战。尽管具有潜力，但高质量数据的收集仍是高精度任务的主要瓶颈。虽然强化学习和仿真到现实的迁移提供了有前途的替代方案，但转移的策略在需要毫米级精度的任务中往往失效，如双臂钢琴演奏。在本文中，我们介绍了HandelBot，一种结合仿真策略和通过两阶段流程实现快速适应的框架。从仿真训练的策略开始，我们首先应用结构化细化阶段，通过调整侧向手指关节来纠正空间对齐，基于物理回放。接着，我们使用残差强化学习自动学习细粒度的纠正动作。通过在五首公认曲目上进行广泛的硬件实验，我们证明HandelBot能够成功进行高精度双手钢琴演奏。我们的系统在真实环境中表现优于直接仿真部署，性能提升达1.8倍，并且仅需30分钟的真实交互数据。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
