Active Learning for Generating Motion and Utterances in Object Manipulation Dialogue Tasks

In an object manipulation dialogue, a robot may misunderstand an ambiguous command from a user, such as “Place the cup down (on the table), ” potentially resulting in an accident. Although making confirmation questions before all motion execution will decrease the risk of this failure, the user will find it more convenient if confirmation questions are not made under trivial situations. This paper proposes a method for estimating ambiguity in commands by introducing an active learning framework with Bayesian logistic regression to human-robot spoken dialogue. We conducted physical experiments in which a user and a manipulator-based robot communicated using spoken language to manipulate objects.