Learning Turn-Taking Behavior from Human Demonstrations for Social Human-Robot Interactions

Abstract

Turn-taking is a fundamental behavior during human interactions and robots must be capable of turn-taking to interact with humans. Current state-of-the-art approaches in turn-taking focus on developing general models to predict the end of turn (EoT) across all contexts. This demands an all-inclusive verbal and non-verbal behavioral dataset from all possible contexts of interaction. Before robot deployment, gathering such a dataset may be infeasible and/or impractical. More importantly, a robot needs to predict the EoT and decide on the best time to take a turn (i.e, start speaking). In this research, we present a learning from demonstration (LfD) system for a robot to learn from demonstrations, after it has been deployed, to make decisions on the appropriate time for taking a turn within specific social interaction contexts. The system captures demonstrations of turn-taking during social interactions and uses these demonstrations to train a LSTM RNN based model to replicate the turn-taking behavior of the demonstrator. We evaluate the system for teaching the turn-taking behavior of an interviewer during a job interview context. Furthermore, we investigate the efficacy of verbal, prosodic, and gestural cues for deciding when to begin a turn.