Learning Algorithm for a Brachiating Robot

This paper introduces a new concept of multi-locomotion robot inspired by an animal. The robot, ‘Gorilla Robot II’, can select the appropriate locomotion (from biped locomotion, quadruped locomotion and brachiation) according to an environment or task. We consider ‘brachiation’ to be one of the most dynamic of animal motions. To develop a brachiation controller, architecture of the hierarchical behaviour-based controller, which consists of behaviour controllers and behaviour coordinators, was used. To achieve better brachiation, an enhanced learning method for motion control, adjusting the timing of the behaviour coordination, is proposed. Finally, it is shown that the developed robot successfully performs two types of brachiation and continuous locomotion.