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

2026-04-14

AI Summary

This work addresses the limited adaptability of existing musculoskeletal robots, whose fixed physiological parameter models hinder performance in complex, dynamic tasks. To overcome this, the authors propose a co-design framework that simultaneously evolves multidimensional muscle properties—including force, velocity, and stiffness—for the first time. By incorporating a low-dimensional spectral manifold derived from bilateral symmetry priors and principal component analysis (PCA), the method drastically reduces the morphological search space. Implemented within the MyoSuite platform via a Spectral Design Evolution (SDE) framework, the approach demonstrates significantly superior performance over both fixed-morphology and standard evolutionary baselines across four locomotion tasks on diverse terrains, achieving higher learning efficiency and movement stability.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Evolutionary Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• musculoskeletal robots
• morphology-control co-design
• physiological parameters
• locomotion performance
• dynamic demands

Innovation

• morphology-control co-design
• Complete Musculoskeletal Morphological Evolution
• Spectral Design Evolution
• muscle parameter optimization
• low-dimensional spectral manifold