Angelique Taylor

Ann-Kareen Gedeus, Jack Good, Nadine Wagener et al.

Embodied conversational agents (ECAs) need effective empathic grounding to foster social support and engagement. Expanding into emotional domains, ECAs now use Large Language Models (LLMs) and multimodal human-agent interactions to enhance their capabilities. Yet, understanding the impact of backchanneling modalities on young adults and their gender remains limited. We introduce TANDE, an LLM-powered ECA designed for emotional conversations with young adults, a population experiencing mental, personal, and social issues with limited tools to address them. In a within-subjects study with N=36 young adults, we explore nonverbal and combined verbal-and-nonverbal backchanneling modalities on rapport, empathy, and engagement and isolate for gender differences. Our research shows the importance of nuanced backchanneling cues with emotional ECAs with young adults, showing a preference for nonverbal cues. We derive design implications for more effective ECAs for emotional support and well-being in young adults. The code is available at https://github.com/Cornell-Tech-AIRLab/TANDE.

Giuliano Pioldi, Yashika Batra, Arman Ibrayeva et al.

Understanding how users perceive and respond to robot failures is essential for building robust and trustworthy robot systems. Prior work, however, (i) often treats failures as independent events, (ii) emphasizes binary failure detection, (iii) with rule-based recovery modeling. We present REPAIR-Bench, built on 214 interaction trials from 41 participants, the benchmark spans four induced failure types and provides synchronized facial action units, head pose, speech transcripts, and post-interaction affect and recovery reports. The benchmark spans three novel evaluation tasks that jointly capture the lifecycle of failure in human-robot interaction (HRI): (i) failure detection over inter-dependent interaction sessions, modeling longitudinal user adaptation across repeated failures; (ii) visual failure-type classification beyond binary success/failure formulations; and (iii) user-centered recovery prediction, inferring users' preferred recovery strategies from interaction context rather than relying on manually designed or rule-based strategies. In baseline experiments, hierarchical recurrent modeling improved failure detection over a single-session model (strict F1: 0.80 vs. 0.68), achieved a failure localization mean signed error of -0.51 s, median absolute error of 2.97 s and, for recovery prediction, a QLoRA-tuned Mistral-7B reached Hit@5=0.76 and F1@5=0.32. REPAIR-Bench provides both the HRI and Medical HRI communities with a standardized framework for (1) evaluating robot failures and (2) building transparent, adaptive, and trustworthy recovery systems.

Promise Ekpo, Angelique Taylor, Lekan Molu

Trauma resuscitation is a clinical process for treating life-threatening physiological disorders in safety-critical environments, driven by the experience of healthcare workers (HCWs). Designing and optimizing quantifiable metrics that accurately capture HCW decisions may augment current resuscitation procedures with the potential to improve patient outcomes. This motivates our socio-technical formulation of trauma resuscitation as a distributed generalized Nash equilibrium (GNE)-seeking game with coupled inequality constraints. This method is optimized over a time-varying communication graph. We introduce novel insights from clinical experience to model HCWs behavior. This work facilitates the best possible resuscitation outcome given HCWs workloads, schedules, competencies, and limited resources.