Curiosity as a Survival Technique

In this paper we present a system that motivates adaptive, curious behavior in a robot in a survival scenario. We continue prior work done on using evolutionary algorithms and neural nets to develop a robot controller capable of collecting energy in a simulated survival environment. Neural nets are efficient for producing survival behavior, but the robot only knows how to respond to stimuli it has seen before it cannot handle situations it has not trained for. In this study, we couple Intelligent Adaptive Curiosity [1] with a NEAT-evolved [2] survival brain to encourage exploration and learning new situations. We find that this “dual-brain” approach encourages intelligent exploration and learning in the robot, which benefits survival, and that the survival brain gives the curious brain time to do this learning. Our system shows a statistically significant increase in survival time as compared to two baseline systems, a pure survival brain and a survival brain augmented by random movements.