Robot command, interrogation and teaching via social interaction

The development of high performance humanoid robots provide complex systems with which humans must interact, and levy serious requirements on the quality and depth of these interactions. At the same time, developments in spoken language technology, and in theories of social cognition and intentional cooperative behavior provide the technical basis and theoretical background respectively for the technical specification of how these systems can work. The objective of the current research is to develop a generalized approach for human-machine interaction via spoken language that exploits recent developments in cognitive science - particularly notions of grammatical constructions as form-meaning mappings in language, and notions of shared intentions as distributed plans for interaction and collaboration. We demonstrate this approach on two distinct robot platforms with human-robot interaction at three levels. The first level is that of commanding or directing the behavior of the system. The second level is that of interrogating or requesting an explanation from the system. The third and most advanced level is that of teaching the machine a new form of behavior. Within this context, we exploit social interaction in two manners. First, the robot identifies different human collaborators, and maintain a permanent record of their interactions in order to treat novices and experts in distinct manners. Second, the interactions are structured around shared intentions that guide the interactions in an ergonomic manner. We explore these aspects of communication on two distinct robotic platforms, the "Event Perceiver" and the Sony Aibo ERS7, and provide in the current paper the state of advancement of this work, and the initial lessons learned.