Musculoskeletal robots offer intrinsic compliance and flexibility, providing a promising paradigm for versatile locomotion. However, existing research typically relies on models with fixed muscle physiological parameters. This static physical setting fails to accommodate the diverse dynamic demands of complex tasks, inherently limiting the robot's performance upper bound. In this work, we focus on the morphology and control co-design of musculoskeletal systems. Unlike previous studies that optimize single physiological attributes such as stiffness, we introduce a Complete Musculoskeletal Morphological Evolution Space that simultaneously evolves muscle strength, velocity, and stiffness. To overcome the exponential expansion of the exploration space caused by this comprehensive evolution, we propose Spectral Design Evolution (SDE), a high-efficiency co-optimization framework. By integrating a bilateral symmetry prior with Principal Component Analysis (PCA), SDE projects complex muscle parameters onto a low-dimensional spectral manifold, enabling efficient morphological exploration. Evaluated on the MyoSuite framework across four tasks (Walk, Stair, Hilly, and Rough terrains), our method demonstrates superior learning efficiency and locomotion stability compared to fixed-morphology and standard evolutionary baselines.

翻译：肌肉骨骼机器人具有内在的柔顺性与灵活性，为多用途运动提供了有前景的范式。然而，现有研究通常依赖肌肉生理参数固定的模型，这种静态物理设定无法适应复杂任务中多样化的动态需求，本质上限制了机器人的性能上限。本研究聚焦于肌肉骨骼系统的形态与控制协同设计。与既往仅优化单一生理属性（如刚度）的研究不同，我们引入了一个完整的肌肉骨骼形态演化空间，可同时演化肌肉力量、速度与刚度。为应对这种全面演化导致的探索空间指数级膨胀，我们提出了谱设计演化（SDE）这一高效协同优化框架。通过结合双侧对称先验与主成分分析（PCA），SDE将复杂肌肉参数投影至低维谱流形，从而实现高效的形态探索。在MyoSuite框架下，我们在四项任务（平地行走、爬楼梯、丘陵与崎岖地形）上的评估表明，与固定形态及标准演化基线相比，我们的方法展现出更优的学习效率与运动稳定性。