Evolving the Complete Muscle: Efficient Morphology-Control Co-design for Musculoskeletal Locomotion
For researchers in musculoskeletal robotics, this work provides a more complete morphological evolution space and an efficient co-optimization method, enabling better performance in complex locomotion tasks.
This paper introduces a morphology-control co-design framework for musculoskeletal robots that simultaneously evolves muscle strength, velocity, and stiffness. The proposed Spectral Design Evolution (SDE) method achieves superior learning efficiency and locomotion stability across four terrains compared to fixed-morphology and standard evolutionary baselines.
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.