  In a robot-centered smart home, the robot observes the home states with its
own sensors, and then it can change certain object states according to an
operator's commands for remote operations, or imitate the operator's behaviors
in the house for autonomous operations. To model the robot's imitation of the
operator's behaviors in a dynamic indoor environment, we use multi-relational
chains to describe the changes of environment states, and apply inverse
reinforcement learning to encoding the operator's behaviors with a learned
reward function. We implement this approach with a mobile robot, and do five
experiments to include increasing training days, object numbers, and action
types. Besides, a baseline method by directly recording the operator's
behaviors is also implemented, and comparison is made on the accuracy of home
state evaluation and the accuracy of robot action selection. The results show
that the proposed approach handles dynamic environment well, and guides the
robot's actions in the house more accurately.
