Learning the Selection of Actions for an Autonomous Social Robot by Reinforcement Learning Based on Motivations

Autonomy is a prime issue on robotics field and it is closely related to decision making. Last researches on decision making for social robots are focused on biologically inspired mechanisms for taking decisions. Following this approach, we propose a motivational system for decision making, using internal (drives) and external stimuli for learning to choose the right action. Actions are selected from a finite set of skills in order to keep robot’s needs within an acceptable range. The robot uses reinforcement learning in order to calculate the suitability of every action in each state. The state of the robot is determined by the dominant motivation and its relation to the objects presents in its environment.