Can the robot "see" what I see? Robot gaze drives attention depending on mental state attribution.

Mentalizing, where humans infer the mental states of others, facilitates understanding and interaction in social situations. Humans also tend to adopt mentalizing strategies when interacting with robotic agents. There is an ongoing debate about how inferred mental states affect gaze following, a key component of joint attention.

Leveraging explainability for understanding object descriptions in ambiguous 3D environments.

For effective human-robot collaboration, it is crucial for robots to understand requests from users perceiving the three-dimensional space and ask reasonable follow-up questions when there are ambiguities. While comprehending the users' object descriptions in the requests, existing studies have focused on this challenge for limited object categories that can be detected or localized with existing object detection and localization modules. Further, they have mostly focused on comprehending the object descriptions using flat RGB images without considering the depth dimension.

Robot-Mediated Inclusive Processes in Groups of Children: From Gaze Aversion to Mutual Smiling Gaze.

Our work is motivated by the idea that social robots can help inclusive processes in groups of children, focusing on the case of children who have newly arrived from a foreign country and their peers at school. Building on an initial study where we tested different robot behaviours and recorded children's interactions mediated by a robot in a game, we present in this paper the findings from a subsequent analysis of the same video data drawing from ethnomethodology and conversation analysis. We describe how this approach differs from predominantly quantitative video analysis in HRI; how mutual gaze appeared as a challenging interactional accomplishment between unacquainted children, and why we focused on this phenomenon.

Lessons Learned About Designing and Conducting Studies From HRI Experts.

The field of human-robot interaction (HRI) research is multidisciplinary and requires researchers to understand diverse fields including computer science, engineering, informatics, philosophy, psychology, and more disciplines. However, it is hard to be an expert in everything. To help HRI researchers develop methodological skills, especially in areas that are relatively new to them, we conducted a virtual workshop, Workshop Your Study Design (WYSD), at the 2021 International Conference on HRI.

Exploring Non-Expert Robot Programming Through Crowdsourcing.

A longstanding barrier to deploying robots in the real world is the ongoing need to author robot behavior. Remote data collection-particularly crowdsourcing-is increasingly receiving interest. In this paper, we make the argument to scale robot programming to the crowd and present an initial investigation of the feasibility of this proposed method.

The role of artificial intelligence in achieving the Sustainable Development Goals.

The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects.