Situated Spoken Dialogue with Robots Using Active Learning

Abstract

Abstract In a human–robot spoken dialogue, the robot may misunderstand an ambiguous command from the user, such as 'Place the cup down (on the table)', thus running the risk of an accident. Although asking confirmation questions before the execution of any motion will decrease the risk of such failure, the user will find it more convenient if confirmation questions are not used in trivial situations. This paper proposes a method for estimating ambiguity in commands by introducing an active learning scheme with Bayesian logistic regression to human–robot spoken dialogue. We conduct physical experiments in which a user and a manipulator-based robot communicate using spoken language to manipulate objects. Keywords: ACTIVE LEARNINGCONFIDENCEROBOT LANGUAGE ACQUISITIONOBJECT MANIPULATION DIALOGUE