Towards autonomous adaptive behavior in a bio-inspired CNN-controlled robot

This paper describes a general approach for the adaptive supervised learning of behaviors in a behavior-based robot. The key idea is to formalize a behavior produced by a Motor Map driven by an internal adaptive reward function. Aim of the adaptive reward function is to select the most significant sensory inputs and to use them in the best way. The greatest challenge is to keep small the search space. Motor map learning relies on the classical Kohonen algorithm, while the structure of the reward function is learnt through a non-associative reinforcement learning algorithm. Simulation results on a six legged biologically-inspired robot confirm the suitability of the approach. This methodology allows the human designer to easily embody all the a priori knowledge on the robot controller, while providing at the same time a high degree of adaptability and robustness against the sensory malfunctioning