Ing. Jan Feber

In this paper, we address the adaptation of the locomotion controller to change of the multi-legged walking robot morphology, such as leg amputation. In nature, the animal compensates for the amputation using its neural locomotion controller that we aim to reproduce with the Central Pattern Generator (CPG). The CPG is a rhythm-generating recurrent neural network used in gait controllers for the rhythmical locomotion of walking robots. The locomotion corresponds to the robot's morphology, and therefore, the locomotion rhythm must adapt if the robot's morphology is changed. The leg amputation can be handled by sensory feedback to compensate for the load distribution imbalances. However, the sensory feedback can be disrupted due to unexpected external events causing the leg to be damaged, thus leading to unexpected motion states. Therefore, we propose dynamic rules for learning a new gait rhythm without the sensory feedback input. The method has been experimentally validated on a real hexapod walking robot to demonstrate its usability for gait adaptation after amputation of one or two legs.

The neurally based gait controllers for multi-legged robots are designed to reproduce the plasticity observed in animal locomotion. In animals, gaits are regulated by Central Pattern Generator (CPG), a recurrent neural network producing rhythmical signals prescribing each leg’s action timing, leading to coordinated motion of multiple legs. The biomimetic CPG-RBF architecture, where leg motion timing is encoded by Radial Basis Function (RBF) neurons coupled with CPG, is used in recent gait controllers. However, the RBF neurons coupling is usually parameterized by the supervisor. Therefore, the RBF parameters get outdated when the CPG signal’s wave-form changes. We propose self-supervised dynamics for RBF parameters adapting to a given CPG and producing the required gait rhythm. The method orders the leg activity with respect to inter-leg coordination rules and maps the activity onto CPG states. The proposed dynamics produce rhythmic control for three different hexapod gaits and adapts to the CPG parametric changes.