Applying homeostatic neural controller to multi-legged robot and adaptivity to novel disruptions

A robot implemented with a homeostatic neural controller can adapt to disruptions that have not been experienced before. When novel changes occur, the homeostatic neural controller autonomously detects the disruptions from their behaviors and recreates new motions to achieve desired behaviors. In previous studies, the homeostatic neural controller has been only applied to a robot with a simple structure, i.e. wheeled robots which are controlled by adjusting the outputs of wheel motors. It is not clear whether the homeostatic neural controller can work properly when it is applied to a robot with a more complex structure. Therefore, we implement the homeostatic neural controller onto the multi-legged robot and investigate adaptivity. As a result, the robot with the homeostatic neural controller recreates new motion patterns to achieve a desired behaviors after novel disruptions that break a leg.