Clustering observed body image for imitation based on value system

In order to develop skills, actions, behavior in a human symbiotic environment, a robot is going to learn something from behavior observation of predecessors or humans. Recently, robotic imitation methods based on many approaches have been proposed. We have proposed reinforcement learning based approaches for the imitation and investigated them under an assumption that the observer recognizes the body parts of the performer and maps them to the ones of its own. However, the assumption is not always applicable because the body of the performer is usually different from the observing robot. In order to learn various behaviors from the observation, the robot has to cluster the observed body area of the performer on the image and maps the clustered parts to its own body parts based on reasonable criterion for itself and feedback the data for the imitation. This paper shows that the clustering the body area on the camera image into the body parts of its own based on the estimation of the state value in the framework of reinforcement learning as well as it imitates the observed behavior based on the state value estimation. The clustering parameters are updated based on the temporal difference error, in an analogous way such that the parameters of the state value of the behavior are updated based on the temporal difference error. The validity of the proposed method is investigated by applying it to a imitation of a dynamic throwing motion of an inverted pendulum robot and human.