Lionel Peter Robert

This panel examines the growing convergence of human–AI interaction (HAI) and human–robot interaction (HRI) as intelligent systems become more autonomous, embodied, and embedded in everyday settings such as healthcare, education, homes, and workplaces. Although HAI and HRI have evolved as distinct fields, the panel argues that shared human factors concerns—such as trust, safety, autonomy, teamwork, situation awareness, evaluation, and responsible deployment—now require greater integration across both domains. Drawing on perspectives from leading researchers and doctoral students, the session explores how embodiment, motion, social cues, and system transparency shape human interaction with AI and robots. It also addresses methodological tensions, including laboratory versus field evaluation and short-term versus longitudinal study, while highlighting implications for ethics, training, and design. Overall, the panel aims to identify common challenges, foster cross-disciplinary collaboration, and outline a research roadmap for human-centered intelligent systems that improve performance, well-being, and quality of life across diverse contexts and sectors.

In human–machine teams, task allocation algorithms typically prioritize competence and other performance-related factors, overlooking human willingness to perform specific tasks. A recent framework proposed incorporating both competence and willingness into task allocation, but its effects on teamwork outcomes remain untested. This article investigates the impact of willingness-based task allocation in a simulated search and rescue (SAR) setting. We conducted a 2 × 2 user study (N = 72) in a 2D grid-world SAR scenario, where human participants collaborated with two virtual robots, which either used a willingness-based or a balanced task allocation strategy. Half the participants were able to change the task allocation prior to the start of the mission. Participants preferred robots and plans that accounted for their preferences, yet willingness-based allocation did not significantly influence trust in the robot or perceived team fluency, as these remained high throughout. Moreover, allowing changes to task allocation improved human performance. Our findings also suggest that the benefits of incorporating willingness may be context-dependent, with greater potential in lower-stakes tasks and longer-term teams.

1. The integration of security robots into public spaces for enhancing safety and enforcing rules has become increasingly prevalent, with deployments in various settings such as malls, airports, ca...

Human-AI teams are gaining traction as they combine the unique strengths of humans and AI. Very little research has examined the unique vulnerability of compromised AI teammates that work directly against the team’s shared goals. Compromised AI attacks combine the potency of technological attacks with teammate betrayal, potentially devastating the team’s ability to execute its goals. Such attacks by a malicious actor may misalign mental models, individual situational awareness, and shared situation awareness. This misalignment could potentially result in the complete loss of team coordination, endangering performance, resiliency, and security. Given the severity of a compromise, this paper presents a model to explain the types of attacks, when they may occur, their effects on the team, and potential approaches for attack identification and recovery.

The growing deployment of autonomous security robots has raised critical questions about how people trust and accept robotic systems exercising public authority. Yet fairness, a central mechanism in public responses to human authority, has received limited attention in the context of robotic authority. Drawing on fairness theory from policing research, this study examines how interactional fairness and distributive fairness influence trust and acceptance of security robots. Using a 2 × 2 between-subjects experiment with 100 U.S. participants, we found that both interactional and distributive fairness significantly increased trust, whereas only interactional fairness significantly increased acceptance. These findings suggest that fairness serves as a critical mechanism shaping public responses to robotic authority. This research extends fairness theory to human-security robot interaction and highlights the importance of clear explanations and consistent rule enforcement for building public trust and acceptance.

The validity of virtually represented robots in HRI experiments depends on when, where, and for whom it matters.