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 nonoptimal 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.

Human balance recovery from external disturbances is a complex process, and simulating it remains an open challenge. In particular, there still is a need for a comprehensive numerical tool capable of predicting the outcome of a balance perturbation, including in particular the three elementary recovery strategies: ankle, hip and stepping with variable step duration. In order to fill this gap we further developed a previously proposed multiple step balance recovery prediction tool to include the use of the hip strategy and variable step duration. Simulated recovery reactions are compared against observations from different experimental situations from the literature. Reasonable accuracy in terms of step positions and durations were obtained for these different situations using a single set of controller parameters. Moreover, variations in the use of the hip strategy and the step duration between situations were consistent with biomechanical observations. Such a model could be useful to better understand the balance recovery mechanisms, and could also be used to identify potentially hazardous situations.

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