Muscle Synergy Priors Enhance Biomechanical Fidelity in Predictive Musculoskeletal Locomotion Simulation
arXiv:2603.10474v2 Announce Type: replace Abstract: Human locomotion emerges from high-dimensional neuromuscular control, making predictive musculoskeletal simulation challenging. We present a physiology-informed reinforcement-learning framework that constrains control using muscle synergies. We extracted a low-dimensional synergy basis from inverse musculoskeletal analyses of a small set of overground walking trials and used it as the action space for a muscle-driven three-dimensional model trained across variable speeds, slopes and uneven terrain. The resulting controller generated stable ga
Advances in reinforcement learning and musculoskeletal modeling are converging, allowing for more physiologically accurate and robust simulations of complex biological systems. This development builds on a growing trend of applying AI to scientific discovery and engineering challenges.
This research provides a significant step towards more sophisticated and reliable predictive biomechanical simulations, which could accelerate progress in robotics, prosthetics, and rehabilitation, all areas with substantial economic and societal implications.
The ability to generate stable, physiologically informed locomotion control for complex 3D models using low-dimensional muscle synergy priors makes predictive simulations more efficient and realistic, reducing the need for extensive real-world experimentation.
- · Robotics industry
- · Medical prosthetics developers
- · Rehabilitation and physical therapy sector
- · AI/ML research institutions
More accurate and robust simulations of human and animal movement become possible, enabling better design of assistive devices and robotic systems.
This could lead to breakthroughs in areas like agile humanoid robots or highly customized and adaptive neuro-prosthetics capable of seamless integration with human intent.
Long-term, this could contribute to a deeper understanding of biological motor control and potentially lead to new forms of human-machine interfaces that leverage muscle synergy principles for intuitive control.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG