A tenacity learning algorithm for humanoid robot locomotion based on the human gait cycle

Abstract

This paper shows a tenacity 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 the learning approach. Each robot motion attempt is available on an action list. Whenever the robot achieves a goal, the algorithm stores the sequence of movements in memory. If there is a failure, the action list provides the position before the fall - and the process starts from that point on. 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.