Behavior Selection of Social Robots Using Developmental Episodic Memory-Based Mechanism of Thought

To use a service robot in a public place, the robot should be able to adapt its behaviors and learn from experiences with people. However, the existing behavior selection algorithms have difficulties in learning deeply or in creating a new behavior. To solve these problems, we implement the human-like thought process, i.e. developmental episodic memory-based mechanism of thought (DEM-MoT), in behavior selection of social robots. In DEM-MoT, a behavior is selected among pre-defined behaviors by an adaptive resonance theory based on the 2-norm Euclidean distance. If the expected reward of the selected behavior is too low, a new behavior is created by randomly arranging a set of primitive behaviors. To show the effectiveness of the proposed behavior selection method, experiments were carried out in a simulated environment.