Behavior learning of a partner robot with a spiking neural network

This paper proposes an on-line learning method for a partner robot. First, the concept of perceiving-acting cycle is applied for learning the relationship between perception and action of a partner robot interacting with its environment. Next, we propose a spiking neural network for learning collision avoiding behavior. The robot learns the forward relationship from sensory inputs to motor outputs as well as the predictive relationship from motor outputs to the sensory inputs. Experimental results show that the robot can learn embodied actions restricted by its physical body.