Turn-taking is one of the main features of communicative systems. In particular, it is one of the bases allowing robust interactions in imitation, thanks to its two linked aspects, i.e., communication and learning. In this article, we propose a simple model based on the interaction of two neural oscillators inhibiting each other which explain how 'turn-taking' may emerge dynamically between two agents. An implementation of the model on a simple robotic platform made of one CCD camera and one simple arm (ADRIANA platform) is detailed. Results showing the emergence of a 'turn-taking' dynamics on this platform are discussed and an extension in simulation for a larger scale of parameters in order to validate robustness is given.

Abstract. Synchrony is claimed by psychology as a crucial parameter of any social interaction: to give to human a feeling of natural interaction, a feeling of agency [17], an agent must be able to synchronise with this human on appropriate time [29] [11] [15] [16] [27]. In the following experiment, we show that synchrony can be more than a state to reach during interaction, it can be a useable cue of the human's satisfaction and level of engagement concerning the ongoing interaction: the better is the interaction, the more synchronous with the agent is the human. We built an architecture that can acquire a human partner's level of synchrony and use this parameter to adapt the agent behavior. This architecture detects temporal relation [1] existing between the actions of the agent and the actions of the human. We used this detected level of synchrony as reinforcement for learning [6]: the more constant the temporal relation between agent and human remains, the more positive is the reinforcement, conversely if the temporal relation varies above a threshold the reinforcement is negative. In a teaching task, this architecture enables naive humans to make the agent learn left-right associations just by the mean of intuitive interactions. The convergence of this learning reinforced by synchrony shows that synchrony conveys current information concerning human satisfaction and that we are able to extract and reuse this information to adapt the agent behavior appropriately.