用微分平坦性降维，让肌肉机器人高效打乒乓球。
Diff-Muscle: Efficient Learning for Musculoskeletal Robotic Table Tennis
- 将高维肌肉激活空间转为低维关节空间，简化控制。
- 在双机器人对打中实现连续回合，成功率显著提升。
- 适合研究复杂运动协调与高效强化学习的学者。
肌骨骼机器人在灵活性和灵巧性方面具有显著优势，是实现具身智能的重要方向。然而，当前研究多局限于简单任务，难以发挥其多段协同的潜力。同时，高维动作空间与过驱动结构导致高效学习困难。为此，我们提出 Diff-Muscle，利用微分平坦性将策略学习从冗余的肌肉激活空间重构为低维关节空间。结合基于运动学的肌肉驱动控制器（K-MAC）与高层轨迹规划，构建分层强化学习框架，使肌骨骼机器人实现灵巧精准的击球。实验表明，Diff-Muscle 在成功率达最优的同时，维持极低的肌肉激活水平。尤其在挑战性的双机器人对打场景中，系统成功实现连续对打。
原文摘要 · Abstract (English)
Musculoskeletal robots provide superior advantages in flexibility and dexterity, positioning them as a promising frontier towards embodied intelligence. However, current research is largely confined to relative simple tasks, restricting the exploration of their full potential in multi-segment coordination. Furthermore, efficient learning remains a challenge, primarily due to the high-dimensional action space and inherent overactuated structures. To address these challenges, we propose Diff-Muscle, a musculoskeletal robot control algorithm that leverages differential flatness to reformulate policy learning from the redundant muscle-activation space into a significantly lower-dimensional joint space. Furthermore, we utilize the highly dynamic robotic table tennis task to evaluate our algorithm. Specifically, we propose a hierarchical reinforcement learning framework that integrates a Kinematics-based Muscle Actuation Controller (K-MAC) with high-level trajectory planning, enabling a musculoskeletal robot to perform dexterous and precise rallies. Experimental results demonstrate that Diff-Muscle significantly outperforms state-of-the-art baselines in success rates while maintaining minimal muscle activation. Notably, the proposed framework successfully enables the musculoskeletal robots to achieve continuous rallies in a challenging dual-robot setting.
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