Acquisition of Probabilistic Behavior Decision Model based on the Interactive Teaching Method

In this paper, we propose a novel method for mobile robots to acquire new autonomous behaviors gradually based on interaction between human and robots. In this method, behavior decision models are constructed using statistical process for experiences of interaction and teaching, and the robot expresses sureness of its own decision using stochastic reasoning. The robot not only decides behavior using the sureness, but also makes suggestions and questions for the user using the sureness. Consequently, the sureness enables the behavior acquisition to be more e ective. We refer to this kind of method as \Interactive Teaching" method. We investigate the feasibility of this method for obstacle avoidance tasks with mobile robots. Through experiments at both virtual and real mobile robot, we have con rmed that the mobile robot acquires robust behavior decision models against changes of environment and uncertainties of sensors, through only several teaching.