Aurélie Clodic

Mary Ellen Foster, Bart Craenen, Amol Deshmukh et al.

In the EU-funded MuMMER project, we have developed a social robot designed to interact naturally and flexibly with users in public spaces such as a shopping mall. We present the latest version of the robot system developed during the project. This system encompasses audio-visual sensing, social signal processing, conversational interaction, perspective taking, geometric reasoning, and motion planning. It successfully combines all these components in an overarching framework using the Robot Operating System (ROS) and has been deployed to a shopping mall in Finland interacting with customers. In this paper, we describe the system components, their interplay, and the resulting robot behaviours and scenarios provided at the shopping mall.

Sandra Devin, Grégoire Milliez, Michelangelo Fiore et al.

The topic of joint actions has been deeply studied in the context of Human-Human interaction in order to understand how humans cooperate. Creating autonomous robots that collaborate with humans is a complex problem, where it is relevant to apply what has been learned in the context of Human-Human interaction. The question is what skills to implement and how to integrate them in order to build a cognitive architecture, allowing a robot to collaborate efficiently and naturally with humans. In this paper, we first list a set of skills that we consider essential for Joint Action, then we analyze the problem from the robot's point of view and discuss how they can be instantiated in human-robot scenarios. Finally, we open the discussion on how to integrate such skills into a cognitive architecture for human-robot collaborative problem solving and task achievement.