DexDrummer: 手持式、接触密集且长周期的灵巧机器人敲击
DexDrummer: In-Hand, Contact-Rich, and Long-Horizon Dexterous Robot Drumming
AI总结：
本文提出DexDrummer，通过模拟训练实现多手灵巧敲击，解决手持、接触密集和长周期操控难题，展示在模拟和现实中的优越性能。
摘要：
在机器人领域，实现手持、接触密集和长周期的灵巧操控仍是一个未解难题。以往手部灵巧性研究各自解决这三个挑战，但未将其整合为单一复杂任务。为进一步测试灵巧性能力，我们提出以敲击作为灵巧操控的测试平台。敲击自然整合了这三个挑战：涉及手指控制稳定和调整鼓棒、通过反复击打鼓面实现接触密集交互、以及在切换鼓和维持节奏时实现长周期协调。我们提出了DexDrummer，一种基于层次化对象中心的双臂敲击策略，通过模拟训练并进行仿真到现实的迁移。该框架通过结合轨迹规划与残差强化学习校正，降低纯强化学习的探索难度，实现快速切换鼓件。一个灵巧操控策略处理接触密集动态，由奖励引导，明确建模手指-鼓棒和鼓棒-鼓面的交互。在模拟中，我们的策略能演奏两种音乐风格：多鼓双臂歌曲和需要更高灵巧性的技术练习。在模拟双臂任务中，我们的灵巧、反应策略在易歌曲中比固定抓取策略高出1.87倍，在难歌曲中高出1.22倍F1分数。在现实任务中，我们展示了多鼓设置下的歌曲表现。DexDrummer能演奏训练歌曲及其扩展版本，F1分数为1.0。
英文摘要：
Performing in-hand, contact-rich, and long-horizon dexterous manipulation remains an unsolved challenge in robotics. Prior hand dexterity works have considered each of these three challenges in isolation, yet do not combine these skills into a single, complex task. To further test the capabilities of dexterity, we propose drumming as a testbed for dexterous manipulation. Drumming naturally integrates all three challenges: it involves in-hand control for stabilizing and adjusting the drumstick with the fingers, contact-rich interaction through repeated striking of the drum surface, and long-horizon coordination when switching between drums and sustaining rhythmic play. We present DexDrummer, a hierarchical object-centric bimanual drumming policy trained in simulation with sim-to-real transfer. The framework reduces the exploration difficulty of pure reinforcement learning by combining trajectory planning with residual RL corrections for fast transitions between drums. A dexterous manipulation policy handles contact-rich dynamics, guided by rewards that explicitly model both finger-stick and stick-drum interactions. In simulation, we show our policy can play two styles of music: multi-drum, bimanual songs and challenging, technical exercises that require increased dexterity. Across simulated bimanual tasks, our dexterous, reactive policy outperforms a fixed grasp policy by 1.87x across easy songs and 1.22x across hard songs F1 scores. In real-world tasks, we show song performance across a multi-drum setup. DexDrummer is able to play our training song and its extended version with an F1 score of 1.0.
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。
