An iterative learning algorithm based on the human gait cycle for locomotion of a humanoid robot

Abstract

This paper shows an iterative learning algorithm that prioritizes static over dynamic stability to decrease gait complexity for an articulated humanoid robot. Initially, we have an array of goal positions, which the algorithm must achieve during the gait cycle. A trial and error process leads to the learning approach. Each robot motion attempt is available on an action list. At the moment the robot achieves a goal, the algorithm stores the sequence of movements in memory. When a failure happens, the action list provides the position just before the fall - and the process starts from that point onwards. In order to test the algorithm, we developed a simulator using Matlab Simulink, together with the Simscape Multibody contact forces library. We present the simulation data through graphs that describe the behavior of the joints during the learning process of a gait cycle.