Evolving spiking neural network for robot locomotion generation

In this paper, we propose locomotion generation for a mobile robot. Legged robot can walk in various complex terrains such as stairs as well as in flat environment. However, setting its behaviour to adapt to various environments in advance is very difficult. The robot can mimic the movement of organisms based on computational intelligence. In this study, we apply spiking neural network, which can take into account the transition of temporal information between the neurons. More specifically, the motion patterns are generated by applying a spiking neural network trained by Hebbian learning and evolution strategy, by using data provided by the physics engine measuring the distance walked by the robot and applied the motion patterns to real robot. Simulation was conducted to confirm the proposed technique.