Anticipative humanoid postural control system for locomotive tasks

Abstract

Postural control of humanoid robots during locomotion tasks has been typically focused in keeping balance by means of classic feedback control systems. The study of human behaviour in postural control reveals the existence of an anticipative component for postural control introduced by means of a feedforward system. This anticipative subsystem is in charge of preparing the human or humanoid system to react against perturbations during locomotion. In this way, the reactions can be more complex and higher perturbations levels can be overcome. The anticipative system is supported by the evaluation of sensory inputs by means of applying concepts extracted from the Surprise Theory. The data captured from sensory sources are transformed into perceptions and then compared with pre-established parameters. The comparison mismatch causes the elicitation of unexpected events or surprises. Then, these events are evaluated to generate a parameter that can be used to fulfil motor reaction patters called synergies. Finally, the new anticipative system is enabled by the use of neuro-fuzzy human style reasoning computing modules.