Adaptive Reference Inverse Optimal Control for Natural Walking With Musculoskeletal Models

An efficient inverse optimal control method named Adaptive Reference IOC is introduced to study natural walking with musculoskeletal models. Adaptive Reference IOC utilizes efficient inner-loop direct collocation for optimal trajectory prediction along with a gradient-based weight update inspired by structured classification in the outer-loop to achieve about 7 times faster convergence than existing derivative-free methods while maintaining similar outcomes in terms of gait trajectory matching. The proposed method adequately reconstructed the reference data when applied to experimental walking data from ten participants walking at various speeds and stride lengths. The proposed framework can facilitate efficient personalized cost function optimization for specific walking tasks, and provide guidance to personalized reference trajectory design for assistive robotic systems such as lower-limb exoskeletons.

Automated summary

An efficient inverse optimal control method called Adaptive Reference IOC is introduced for studying natural walking using musculoskeletal models. The method achieves about 7 times faster convergence compared to existing derivative-free methods while maintaining similar outcomes in terms of gait trajectory matching. The proposed method successfully reconstructs reference data when applied to experimental walking data and can provide guidance for personalized cost function optimization and reference trajectory design for assistive robotic systems.