Does a robot’s gaze aversion affect human gaze aversion?

Gaze cues serve an important role in facilitating human conversations and are generally considered to be one of the most important non-verbal cues. Gaze cues are used to manage turn-taking, coordinate joint attention, regulate intimacy, and signal cognitive effort. In particular, it is well established that gaze aversion is used in conversations to avoid prolonged periods of mutual gaze. Given the numerous functions of gaze cues, there has been extensive work on modelling these cues in social robots. Researchers have also tried to identify the impact of robot gaze on human participants. However, the influence of robot gaze behavior on human gaze behavior has been less explored. We conducted a within-subjects user study (N = 33) to verify if a robot’s gaze aversion influenced human gaze aversion behavior. Our results show that participants tend to avert their gaze more when the robot keeps staring at them as compared to when the robot exhibits well-timed gaze aversions. We interpret our findings in terms of intimacy regulation: humans try to compensate for the robot’s lack of gaze aversion.

Games At Work dot Biz e513 — Not Dead Yet

A weekly podcast and blog where we focus on metaverse & gaming technology in business, gamification and the business of games.

Journal article on human–robot collaboration in industry - OFAI

Emergent dialogue | Proceedings of the 2014 conference on Interaction design and children

Reducing Computational Cost During Robot Navigation and Human–Robot Interaction with a Human-Inspired Reinforcement Learning Architecture - International Journal of Social Robotics

We present a new neuro-inspired reinforcement learning architecture for robot online learning and decision-making during both social and non-social scenarios. The goal is to take inspiration from the way humans dynamically and autonomously adapt their behavior according to variations in their own performance while minimizing cognitive effort. Following computational neuroscience principles, the architecture combines model-based (MB) and model-free (MF) reinforcement learning (RL). The main novelty here consists in arbitrating with a meta-controller which selects the current learning strategy according to a trade-off between efficiency and computational cost. The MB strategy, which builds a model of the long-term effects of actions and uses this model to decide through dynamic programming, enables flexible adaptation to task changes at the expense of high computation costs. The MF strategy is less flexible but also 1000 times less costly, and learns by observation of MB decisions. We test the architecture in three experiments: a navigation task in a real environment with task changes (wall configuration changes, goal location changes); a simulated object manipulation task under human teaching signals; and a simulated human–robot cooperation task to tidy up objects on a table. We show that our human-inspired strategy coordination method enables the robot to maintain an optimal performance in terms of reward and computational cost compared to an MB expert alone, which achieves the best performance but has the highest computational cost. We also show that the method makes it possible to cope with sudden changes in the environment, goal changes or changes in the behavior of the human partner during interaction tasks. The robots that performed these experiments, whether real or virtual, all used the same set of parameters, thus showing the generality of the method.