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.

翻译：肌肉骨骼机器人在灵活性和灵巧性方面具有显著优势，使其成为实现具身智能的一个前沿方向。然而，当前研究主要局限于相对简单的任务，限制了对多关节协调中其全部潜力的探索。此外，高效学习仍然是一个挑战，这主要源于高维动作空间和固有的过驱动结构。为解决这些挑战，我们提出了Diff-Muscle，一种利用微分平坦性将策略学习从冗余的肌肉激活空间重新表述到维度显著降低的关节空间的肌肉骨骼机器人控制算法。此外，我们利用高度动态的机器人乒乓球任务来评估我们的算法。具体而言，我们提出了一种分层强化学习框架，该框架将基于运动学的肌肉驱动控制器与高层轨迹规划相结合，使肌肉骨骼机器人能够执行灵巧而精准的对打。实验结果表明，Diff-Muscle在成功率上显著优于现有最先进的基线方法，同时保持最小的肌肉激活。值得注意的是，所提出的框架成功使肌肉骨骼机器人在具有挑战性的双机器人设置中实现了连续对打。