Human-Robot greeting: tracking human greeting mental states and acting accordingly

Abstract

Mobile social robots should be able to engage in interaction with people effectively. However, greeting someone is a complex task since it implies an exchange of social signals. Adam Kendon modeled human greetings as a set of six phases: initiation of approach, distance salutation, head dip, approach, final approach, and close salutation. Based on Kendon’s model, we propose a system for mobile social robots that manages the greeting process through the exchange of social signals. A Hidden Markov Model keeps track of the greeting stage through the observation of the human gestures, while a behavior tree generates appropriate robot actions. We used publicly available datasets to train the Hidden Markov Model. Evaluation on test sets showed an average greeting phase estimation accuracy of 80.9%. We tested the full system (Hidden Markov Model + Behavior Tree) in simulation and in a real world pilot experiment using the Vizzy robot, and it recognized and replicated the correct phase with an accuracy of 91.8% and 53.8%, respectively.