Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity

2026-03-31

AI Summary

Accurately inferring neuromuscular control strategies from observed kinematics in high-dimensional, redundant musculoskeletal systems remains a formidable challenge. This work proposes a large-scale parallel musculoskeletal simulation framework that integrates GPU-accelerated dynamics, adversarial reward aggregation, and value-guided flow exploration to overcome optimization bottlenecks in high-dimensional reinforcement learning for muscle control. For the first time, the method simultaneously achieves high-fidelity motion tracking—accurately reproducing joint angles and body positions—and discovers diverse neuromuscular control policies across complex dynamic tasks such as dancing, cartwheels, and backflips. The results reveal that markedly distinct muscle activation patterns can produce nearly identical external movement outcomes, highlighting the inherent redundancy and flexibility of biological motor control.

Abstract

The embodied learning of human motor control requires whole-body neuro-actuated musculoskeletal dynamics, while the internal muscle-driven processes underlying movement remain inaccessible to direct measurement. Computational modeling offers an alternative, but inverse dynamics methods struggled to resolve redundant control from observed kinematics in the high-dimensional, over-actuated system. Forward imitation approaches based on deep reinforcement learning exhibited inadequate tracking performance due to the curse of dimensionality in both control and reward design. Here we introduce a large-scale parallel musculoskeletal computation framework for biomechanically grounded whole-body motion reproduction. By integrating large-scale parallel GPU simulation with adversarial reward aggregation and value-guided flow exploration, the MS-Emulator framework overcomes key optimization bottlenecks in high-dimensional reinforcement learning for musculoskeletal control, which accurately reproduces a broad repertoire of motions in a whole-body human musculoskeletal system actuated by approximately 700 muscles. It achieved high joint angle accuracy and body position alignment for highly dynamic tasks such as dance, cartwheel, and backflip. The framework was also used to explore the musculoskeletal control solution space, identifying distinct musculoskeletal control policies that converge to nearly identical external kinematic and mechanical measurements. This work establishes a tractable computational route to analyzing the specificity and diversity underlying human embodied control of movement. Project page: https://lnsgroup.cc/research/MS-Emulator.

Problem

• musculoskeletal control
• whole-body motion reproduction
• motor control diversity
• high-dimensional redundancy
• embodied learning

Innovation

• musculoskeletal emulation
• deep reinforcement learning
• parallel simulation
• adversarial reward aggregation
• value-guided exploration