Interacting with Pepper: mutual learning of turn-taking practices in HRI

PepperMint (Interacting with Pepper: mutual learning of turn-taking practices in HRI) is funded by the ASLAN Labex. It proposes an explorative study of embodied turn-taking practices in task-related Human-Robot Interaction (HRI) to improve the social abilities of robots and make HRI more natural to humans. The project initiates a cooperation between researchers in AI (Artificial Intelligence) (LIRIS) and CA (Conversation Analysis) (ICAR and GenZ Oulu - Finland). It investigates if and how CA findings on natural occurring interaction can be used to develop innovative and effective AI models for HRI. The project is grounded in a detailed multimodal analysis of turn-taking in naturally occurring HRI, putting forward the emergence of turn allocation as complex sequential and multimodal practices.

The project will build upon existing works on AI/ML (Machine Learning) algorithms of the state of the art to program an application for reception and orientation of people in an university library. This application will be used for the recording of human-robot interactions and turn-taking practices, that will be used in CA studies to identify successful interactions. The outcome of the CA studies will be used to develop a new version of the robot’s algorithms, with the objective to improve HRI.