Social norms regulate actions in artificial societies, steering collective behavior towards desirable states. In real societies, social norms can solve cooperation dilemmas, constituting a key ingredient in systems of indirect reciprocity: reputations of agents are assigned following social norms that identify their actions as good or bad. This, in turn, implies that agents can discriminate between the different actions of others and that the behaviors of each agent are known to the population at large. This is only possible if the agents report their interactions. Reporting constitutes, this way, a fundamental ingredient of indirect reciprocity, as in its absence cooperation in a multiagent system may collapse. Yet, in most studies to date, reporting is assumed to be cost-free, which collides with many life situations, where reporting can easily incur a cost (costly reputation building). Here we develop a new model of indirect reciprocity that allows reputation building to be costly. We show that only two norms can sustain cooperation under costly reputation building, a feature that requires agents to be able to anticipate the reporting intentions of their opponents, depending sensitively on both the cost of reporting and the accuracy level of reporting anticipation.

This paper presents an ongoing study in the area of Human-Robot Collaboration, more precisely collaborative manipulation tasks between one robot and multiple people. We study how different trajectories influence people’s perception of the robot’s goal. To achieve this, we propose an approach based on Probabilistic Motor Primitives and the notion of legibility and predictability of trajectories to create the movements the robot performs during task execution. In this approach we also propose combining of legible and predictable trajectories depending on the state of the task in order to diminish the drawbacks associated with each type of trajectory.

Human-Robot Interaction (HRI) is a highly multidisciplinary endeavor. However, it often still appears to be an effort driven primarily by technical aims and concerns. We outline some of the major challenges for fruitful interdisciplinary collaboration in HRI, arguing for an improved integration of psychology and applied social sciences and their genuine research agendas. Based on our own disciplinary backgrounds, we discuss these issues from vantage points mostly originating in applied engineering and psychology, but also from relevant related fields such as sociology, communication sciences, philosophy, arts, and design. We take a project-case as an example to discuss grounded and practical challenges in HRI research, and to propose how a combination of artificial intelligence advances and a better conceptual definition of the role of social sciences in HRI research may prove to be beneficial. Our goal is to strengthen the impact and effectiveness of social scientists working in HRI, and thereby better prepare the field for future challenges.

We are looking at new ways of building algorithms for synthesizing and rendering animation in social robots that can keep them as interactive as necessary, while still following on principles and practices used by professional animators. We will be studying the animation process side by side with professional animators in order to understand how these algorithms and tools can be used by animators to achieve animation capable of correctly adapting to the environment and the artificial intelligence that controls the robot. Figure 1: Two example scenarios featuring a touch-based Robotic characters are becoming widespread as useful multimedia application, sensors, and different robots.

Eye contact is a crucial behaviour in human communication and therefore an essencial feature in human-robot interaction. A study regarding the development of an eye behaviour model for a robotic tutor in a task-oriented environment is presented, along with a description of how our proposed model is being used to implement an autonomous robot in the EMOTE project.