Hierarchical learning of reactive behaviors in an autonomous mobile robot

Describes an autonomous mobile robot that employs a simple sensorimotor learning algorithm at three different behavioral levels to achieve coherent goal-directed behavior. The robot autonomously navigates to a goal destination within an obstacle-ridden environment by using the learned behaviors of obstacle-detection, obstacle-avoidance, and beacon-following. These reactive behaviors are learned in a hierarchical manner by using a hillclimbing routine that attempts to find the optimal transfer function from perceptions to actions for each behavior. The authors present experimental results that show that each behavior was successfully learned by the robot within a reasonably short period of time. The authors conclude by discussing salient features of their approach and possible directions for future research