User adaptation of human-robot interaction model based on Bayesian network and introspection of interaction experience

We propose a behavior learning method based on Bayesian networks and experience of interaction between human and robots, which does not need a priori knowledge and can be applied to human-robot interaction models. In this method, the behavior learning based on interaction experience was established. However, developers must adjust initial sensor state of the Bayesian network according to the user preference. In this paper, we propose a new method of state space construction for user adaptation based on introspection of interaction experience using genetic algorithms. We also give two examples: 1) obstacle avoidance tasks for mobile robots; and 2) symbol grounding for natural language instruction, for realization of user's adaptation of human-robot interaction.