Memory and learning for social robots

While most research in social robotics embraces the challenge of designing and studying the interaction between robots and humans itself, this talk will discuss the utility of social interaction in order to facilitate for more flexible robotics. What can a robot gain with respect to learning and adaptation from being able to sociably interact? What are basic learning-enabling behaviors? And how do inexperienced human tutor robots a sociable way? In order to answer these question we consider the challenge of learning by interaction as a systemic one, comprising appropriate perception, system design, and feedback. Basic abilities of robots will be outlined which resemble concepts of developmental learning in infants, apply linguistic models of interaction management, and take tutoring as a joint task of a human and a robot. However, in order to tackle the challenge of learning by interaction the robot has to couple and coordinate these behaviors in a very flexible and adaptive manner. The active memory as an architectural concept in particular suitable for learning-enabled robots will be briefly discussed as a foundation for coordination and integration of such interactive robotic systems. The talk will build a bridge from the construction of integrated robotic systems to their evaluation, analysis, and way back. It will outline why we intend to enable our robots to learn by interacting and how this paradigm impacts the design of systems and interaction behaviors.