This research develops digital twin systems to personalise robotic exoskeleton movement. By integrating biomechanical modelling with real-time robotic control, it enables adaptive, user-specific walking patterns. The approach aims to improve rehabilitation outcomes by making assistive devices more natural, responsive, and aligned with individual movement needs.

This talk explains research that teaches legged robots how to walk reliably using machine learning, computer vision, advanced control theory, and Lyapunov-based safety guarantees. By improving robot stability on complex terrain, the work moves us closer to versatile, household multi-purpose robots capable of performing everyday chores safely and independently.