Evolving the Complete Muscle: Efficient Morphology-Control Co-design for Musculoskeletal Locomotion

TL;DR

This paper introduces a novel co-design framework for musculoskeletal robots that evolves multiple muscle parameters simultaneously, significantly improving locomotion performance and efficiency across diverse terrains.

Contribution

It presents the Complete Musculoskeletal Morphological Evolution Space and Spectral Design Evolution (SDE), enabling efficient exploration of complex muscle parameter combinations.

Findings

Outperforms fixed-morphology approaches in learning efficiency.

Achieves more stable and versatile locomotion across tasks.

Uses PCA and symmetry priors for low-dimensional exploration.

Abstract

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.