Co-creation of human-robot interaction rules through response prediction and habituation/dishabituation

A joint learning approach is described that meets a major challenge with social robots — developing a methodology for learning communicative behaviors. We focus on interaction rule that is relationship between a robot's action and a partner's response. In this approach a robot is simultaneously a learner and proposer of interaction rules. The human partner and robot continuously search for and co-create new rules as inspired by the social games played between an infant and a caregiver. A simple and universal scheme with response prediction and habituation/dishabituation was developed, and a robot model was built using the scheme. The robot generates actions, observes the partner's response, and get to predict them. It identifies relationships between its actions and the responses, and generates actions designed to elicit particular responses from the partner. After it is habituated to the responses, it generates other actions to search for other rules. In experiments of human-robot interaction based on this model and using a ball, different patterns of interaction emerged, such as passing the ball back and forth, rolling and catching, and feint passing. Response prediction and appropriate habituation supported the emergence of interactions, indicating that the scheme and the model are effective. This joint learning should lead to natural communication between human partners and social robots beyond teach/taught relationship.