Learning to interact and interacting to learn: Active statistical learning in human-robot interaction

Abstract

Learning and interaction are viewed as two related but distinct topics in developmental robotics. Many studies focus solely on either building a robot that can acquire new knowledge and learn to perform new tasks, or designing smooth human-robot interactions with pre-acquired knowledge and skills. The present paper focuses on linking language learning with human-robot interaction, showing how better human-robot interaction can lead to better language learning by robot. Toward this goal, we developed a real-time human-robot interaction paradigm in which a robot learner acquired lexical knowledge from a human teacher through free-flowing interaction. With the same statistical learning mechanism in the robot's system, we systematically manipulated the degree of activity in human-robot interaction in three experimental conditions: the robot learner was either highly active with lots of speaking and looking acts, or moderately active with a few acts, or passive without actions. Our results show that more talking and looking acts from the robot, including those immature behaviors such as saying non-sense words or looking at random targets, motivated human teachers to be more engaged in the interaction. In addition, more activities from the robot revealed its robot's internal learning states in real time, which allowed human teachers to provide more useful and "on-demand" teaching signals to facilitate learning. Thus, compared with passive and batch-mode training, an active robot learner can create more and better training data through smooth and effective social interactions that consequentially lead to more successful language learning.