Ankle, hip and stepping strategies for humanoid balance recovery with a single Model Predictive Control scheme

While humans are highly efficient in dealing with balance perturbations, current biped humanoid robots are far from showing similar skills. This is mainly due to the limited capacity of current robot controllers to deal with the inherently complex dynamics of biped robots. Though Model Predictive Control schemes have shown improved robustness to perturbations, they still suffer from a few shortcomings such as not considering the upper body inertial effects or non-optimal step durations. We propose here a Model Predictive Control scheme that specifically addresses these shortcomings and generates human-like responses to perturbations, involving appropriate combinations of ankle, hip and stepping strategies, with automatically adjusted step durations. The emphasis of this paper is on modeling and analyzing the effects of different cost functions and coefficients on the behavior of the controller while leaving real-time implementations and experiments for later work.