Incremental learning of human social behaviors with feature-based spatial effects

In the near future, robots will exist in our home, office, hospital, school and all other public places. In other words, robots have to work and share environments with human. However, there exist many invisible social rules or social protocols in human society. For example, people usually stand in a line to access certain services. It seems that there is a social force or a “spatial effect” which forces people to perform the behavior of standing in a line. Unfortunately, these spatial effects are usually invisible and immeasurable. It makes social behavior understanding to be a difficult problem. The robot that does not comprehend these spatial effects might harm people or itself. Previous methods of behavior understanding mainly only targeted certain scenarios or environments. This paper follows the framework of SBCM (Spatial Behavior Cognition Model)[3] and extends to feature-based spatial effects. By utilizing feature to represent the spatial effect, it is easier to apply known spatial effects to different environments. This paper also demonstrates that the robot is able to learn and execute some common social behaviors under the proposed method. Several simulations and experiments are conducted to verify the proposed idea in this paper.