arXiv subjects

Angelique Taylor

Publications and source records attributed to Angelique Taylor.

At least 19 recordsLinked to original sources

arXiv2026

Toward Human-in-the-Loop Robot Failure Recovery: Bridging Communication Gaps in Human-Robot Collaboration

Robots can recover from failures by asking bystanders for help, but effective human-in-the-loop recovery requires communication that accounts for differences in people's knowledge. Prior inverse-semantics work generates requests using a single listener model, leaving differences in listener knowledge untested. We introduce Listener Differences in Human-Robot Interaction (LD-HRI), a game, dataset, and benchmark that evaluates speakers through human listener performance. Our evaluation examines request properties, large language model (LLM) speakers, and inverse-semantics request-selection algorithms under controlled differences in listener information. The corpus contains 446 human-written requests and 1{,}302 listener trials. We additionally evaluated 24 frozen LLM-written requests with 70 human listeners across 560 trials. Novice success is descriptively higher with model-written requests across all four tasks, yet both request sources leave substantial expert--novice gaps, including 16 percentage points for LLM requests. LD-HRI makes these gaps measurable, providing a foundation for designing more robust communication in human-robot and human-agent interaction.

cs.RO↗

arXiv2026

Finishing the Task Is Not Enough: Evaluating Agent Resilience and Considerate Participation under Accumulating Challenge

Sustained deployment of generative AI agents requires more than isolated task success. Agents must remain useful across repeated interactions, changing conditions, and dependencies on people within shared workflows, especially as technical, human, and operational disruptions accumulate over time. We propose operational resilience and considerate participation as two complementary aspects of evaluating such agents: the former captures how agents recover from blocked work while preserving progress and communicating their limits, and the latter captures how their adaptation accounts for affected people, role boundaries, and the surrounding workflow. Yet both remain underexplored under accumulating challenge. We study 120 simulated healthcare trajectories across two generative AI models and twelve stakeholder-derived tasks under light, medium, and heavy challenge. We compare textual action plans, prompted internal assessments, and quantitative structured workload and affect reports to examine how agent behavior and reported state change as challenge accumulates. Regarding operational resilience, agents shift from self-directed recovery toward greater human dependence, while reporting increasing workload and negative affect in structured reports but seldom expressing strain in textual responses. Regarding considerate participation, agents broaden from task-focused adaptation toward task reframing, attention to others, role-boundary adjustment, and wider coordination, with distinct patterns across actions and internal assessments. From these findings, we derive five deployment dilemmas involving persistence, attention, role boundaries, state disclosure, and escalation that require stakeholder specification, further informing technical implications for learning, situated evaluation, and embodied adaptation.

cs.AI↗

arXiv2026

Code Black: Desktop-Mediated Co-Design of AR-HMD Microinteractions for Emergency Department Teamwork

Emergency Department (ED) teams coordinate shifting roles, medication decisions, and time-critical interventions under uncertainty. Augmented reality head-mounted displays (AR-HMDs) have shown potential to spatially anchor information during care, creating opportunities to examine how spatial interfaces might support teamwork. We conducted a speculative co-design study with 12 healthcare workers (HCWs) using an editable, desktop-mediated Unity-based 3D design probe to visualize and refine work-as-imagined AR-HMD interfaces for role-based notifications, task-specific timers, and dosage verification. Guided by microinteraction rules, participants identified future spatial user interfaces (SUI) requirements such as how they appear, update, or are dismissed in relation to clinical practice, safety concerns, and existing tools. Five returning participants and 26 additional HCWs subsequently provided follow-up feedback on derived visual interface alternatives. Findings show that desktop-mediated spatial co-design elicited formative specifications for role visibility, task-linked timing, and verification-oriented dosage assistance, while revealing tensions involving clutter, shared awareness, communication, privacy, and reliability. Rather than evaluating a functional AR-HMD system or team-based clinical performance, this study contributes the Speculative Co-Design Framework for AR-HMD Teamwork (SCF-HMD) and a visual design catalog for translating expert critique of work-as-imagined (WAI) concepts into situated goals for future AR-HMD systems.

cs.HC↗

arXiv2026

Alignment Under Pressure: AR-HMD Support Tools for Action Teams

Team communication breakdowns represent a contributor to patient safety risks within action teams-defined as interdependent groups of specialized people who perform coordinated work under high workload, time pressure, and uncertainty. Approximately 70% of such instances lead to adverse patient outcomes amid intense time pressure, uncertainty, and high cognitive load. While prior research has focused on maintaining shared cognition during these interactions, existing technologies largely prioritize individual task execution and decision-making, offering limited support for real-time team coordination. This study investigates the potential of augmented reality head-mounted displays (AR-HMDs) to address this gap by facilitating what we call 'team alignment' - the active maintenance of shared understanding regarding tasks, patient state, responsibilities, and ongoing clinical activity. Through an 11-month multi-phase qualitative study with ten healthcare professionals, we first elicited coordination challenges through semi-structured interviews complemented by real-time storyboard creation. Participants then engaged in reflection and refinement of these scenarios while contemplating the potential impact of AR-HMDs on their situation. Our findings revealed that breakdowns frequently arose when clinicians lacked sufficient contextual information, when assigned responsibilities did not align with available expertise, or when procedural progress was difficult to track - particularly during critical bedside activity. We subsequently developed the Team Alignment and Coordination Taxonomy (TACT), encompassing information, expertise, procedural, and cognitive dimensions. By reframing coordination as this active maintenance of alignment, our research shifts the design focus from individual decision support systems to holistic, team-level system interventions.

cs.HC↗

arXiv2026

TANDE: Disentangling Verbal and Nonverbal Backchannels in Emotional AI-Avatar Conversations with Young Adults

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.

cs.HC↗

arXiv2026

REPAIR-Bench: A Benchmark for Robot Error Perception And Interaction Recovery

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.

cs.RO↗

arXiv2026

Towards Considerate Human-Robot Coexistence: A Dual-Space Framework of Robot Design and Human Perception in Healthcare

The rapid advancement of robotics is reshaping what it means for humans and robots to coexist -- through expanded capabilities, more intuitive interactions, and deeper integration into real-world workflows. Beyond sharing physical space, this coexistence is increasingly characterized by organizational embeddedness, temporal evolution, social situatedness, and open-ended uncertainty. Because such coexistence extends beyond a single encounter, understanding healthcare robots requires looking beyond initial acceptance to how stakeholders' perceptions evolve through continued engagement. Yet, prior work has largely relied on single-point snapshots of attitudes and acceptance, offering limited insight into coexistence as a long-term, dynamic process. We address these gaps through in-depth follow-up interviews with nine participants from a 14-week co-design study on healthcare robots. We identify the human perception space, which includes four interpretive dimensions (i.e., degree of decomposition, source of evidence, scope of reasoning, and temporal orientation). We enrich the conceptual framework of human-robot coexistence by conceptualizing the mutual relationship between the human perception space and the robot design space as a co-evolving loop, in which human needs, design decisions, situated interpretations, and social mediation continuously reshape one another over time. Building on this, we propose considerate human-robot coexistence, arguing that humans act not only as design contributors but also as interpreters and mediators who actively shape how robots are understood and integrated across deployment stages. Our related prior work and supplementary materials, including the interview protocol, are available at https://byc-sophie.github.io/considerate-human-robot-coexistence/

cs.RO↗

arXiv2026

A Generalized Nash Equilibrium-Seeking Scheme for Trauma Resuscitation

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.

cs.MA↗

arXiv2026

AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning

Fair workload enforcement in heterogeneous multi-agent systems that pursue shared objectives remains challenging. Fixed fairness penalties often introduce inefficiencies, training instability, and conflicting agent incentives. Reward-shaping approaches in fair Multi-Agent Reinforcement Learning (MARL) typically incorporate fairness through heuristic penalties or scalar reward modifications and often rely on post-hoc evaluation. However, these methods do not guarantee that a desired fairness level will be satisfied. To address this limitation, we propose the Adaptive Fairness Multi-Agent Reinforcement Learning (AdaFair-MARL) framework, which formulates workload fairness as an explicit constraint so that agents maintain balanced contributions while optimizing team performance. We present AdaFair-MARL, a constrained cooperative MARL framework whose core algorithmic component is a primal-dual update that enforces workload fairness via adaptive Lagrange multiplier updates. Grounding the framework in a cooperative Markov game, we derive the fairness constraint from Jain's Fairness Index (JFI) geometry and show that the resulting feasible set admits a second-order cone representation, enabling principled Lagrangian dual-ascent updates without manual penalty tuning. Experiments in a simulated hospital coordination environment (MARLHospital) demonstrate the effectiveness of AdaFair-MARL compared to reward-shaping and fixed-penalty fairness methods, improving workload balance while maintaining team performance. We found that AdaFair-MARL achieves nearly perfect constraint satisfaction (0.99-1.00) while significantly improving workload fairness compared to fixed-penalty baselines.

cs.LG↗

arXiv2026

Before Humans Join the Team: Diagnosing Coordination Failures in Healthcare Robot Team Simulation

As humans move toward collaborating with coordinated robot teams, understanding how these teams coordinate and fail is essential for building trust and ensuring safety. However, exposing human collaborators to coordination failures during early-stage development is costly and risky, particularly in high-stakes domains such as healthcare. We adopt an agent-simulation approach in which all team roles, including the supervisory manager, are instantiated as LLM agents, allowing us to diagnose coordination failures before humans join the team. Using a controllable healthcare scenario, we conduct two studies with different hierarchical configurations to analyze coordination behaviors and failure patterns. Our findings reveal that team structure, rather than contextual knowledge or model capability, constitutes the primary bottleneck for coordination, and expose a tension between reasoning autonomy and system stability. By surfacing these failures in simulation, we prepare the groundwork for safe human integration. These findings inform the design of resilient robot teams with implications for process-level evaluation, transparent coordination protocols, and structured human integration. Supplementary materials, including codes, task agent setup, trace outputs, and annotated examples of coordination failures and reasoning behaviors, are available at: https://byc-sophie.github.io/mas-to-mars/.

cs.RO↗

arXiv2026

RFM-HRI : A Multimodal Dataset of Medical Robot Failure, User Reaction and Recovery Preferences for Item Retrieval Tasks

While robots deployed in real-world environments inevitably experience interaction failures, understanding how users respond through verbal and non-verbal behaviors remains under-explored in human-robot interaction (HRI). This gap is particularly significant in healthcare-inspired settings, where interaction failures can directly affect task performance and user trust. We present the Robot Failures in Medical HRI (RFM-HRI) Dataset, a multimodal dataset capturing dyadic interactions between humans and robots embodied in crash carts, where communication failures are systematically induced during item retrieval tasks. Through Wizard-of-Oz studies with 41 participants across laboratory and hospital settings, we recorded responses to four failure types (speech, timing, comprehension, and search) derived from three years of crash-cart robot interaction data. The dataset contains 214 interaction samples including facial action units, head pose, speech transcripts, and post-interaction self-reports. Our analysis shows that failures significantly degrade affective valence and reduce perceived control compared to successful interactions. Failures are strongly associated with confusion, annoyance, and frustration, while successful interactions are characterized by surprise, relief, and confidence in task completion. Emotional responses also evolve across repeated failures, with confusion decreasing and frustration increasing over time. This work contributes (1) a publicly available multimodal dataset (RFM-HRI), (2) analysis of user responses to different failure types and preferred recovery strategies, and (3) a crash-cart retrieval scenario enabling systematic comparison of recovery strategies with implications for safety-critical failure recovery. Our findings provide a foundation for failure detection and recovery methods in embodied HRI.

cs.RO↗

arXiv2026

Towards Considerate Embodied AI: Co-Designing Situated Multi-Site Healthcare Robots from Abstract Concepts to High-Fidelity Prototypes

Co-design is essential for grounding embodied artificial intelligence (AI) systems in real-world contexts, especially high-stakes domains such as healthcare. While prior work has explored multidisciplinary collaboration, iterative prototyping, and support for non-technical participants, few have interwoven these into a sustained co-design process. Such efforts often target one context and low-fidelity stages, limiting the generalizability of findings and obscuring how participants' ideas evolve. To address these limitations, we conducted a 14-week workshop with a multidisciplinary team of 22 participants, centered around how embodied AI can reduce non-value-added task burdens in three healthcare settings: emergency departments, long-term rehabilitation facilities, and sleep disorder clinics. We found that the iterative progression from abstract brainstorming to high-fidelity prototypes, supported by educational scaffolds, enabled participants to understand real-world trade-offs and generate more deployable solutions. We propose eight guidelines for co-designing more considerate embodied AI: attuned to context, responsive to social dynamics, mindful of expectations, and grounded in deployment. Project Page: https://byc-sophie.github.io/Towards-Considerate-Embodied-AI/

cs.HC↗

arXiv2025

Skill-Aligned Fairness in Multi-Agent Learning for Collaboration in Healthcare

Fairness in multi-agent reinforcement learning (MARL) is often framed as a workload balance problem, overlooking agent expertise and the structured coordination required in real-world domains. In healthcare, equitable task allocation requires workload balance or expertise alignment to prevent burnout and overuse of highly skilled agents. Workload balance refers to distributing an approximately equal number of subtasks or equalised effort across healthcare workers, regardless of their expertise. We make two contributions to address this problem. First, we propose FairSkillMARL, a framework that defines fairness as the dual objective of workload balance and skill-task alignment. Second, we introduce MARLHospital, a customizable healthcare-inspired environment for modeling team compositions and energy-constrained scheduling impacts on fairness, as no existing simulators are well-suited for this problem. We conducted experiments to compare FairSkillMARL in conjunction with four standard MARL methods, and against two state-of-the-art fairness metrics. Our results suggest that fairness based solely on equal workload might lead to task-skill mismatches and highlight the need for more robust metrics that capture skill-task misalignment. Our work provides tools and a foundation for studying fairness in heterogeneous multi-agent systems where aligning effort with expertise is critical.

cs.MA↗

arXiv2025

CARIS: A Context-Adaptable Robot Interface System for Personalized and Scalable Human-Robot Interaction

The human-robot interaction (HRI) field has traditionally used Wizard-of-Oz (WoZ) controlled robots to explore navigation, conversational dynamics, human-in-the-loop interactions, and more to explore appropriate robot behaviors in everyday settings. However, existing WoZ tools are often limited to one context, making them less adaptable across different settings, users, and robotic platforms. To mitigate these issues, we introduce a Context-Adaptable Robot Interface System (CARIS) that combines advanced robotic capabilities such teleoperation, human perception, human-robot dialogue, and multimodal data recording. Through pilot studies, we demonstrate the potential of CARIS to WoZ control a robot in two contexts: 1) mental health companion and as a 2) tour guide. Furthermore, we identified areas of improvement for CARIS, including smoother integration between movement and communication, clearer functionality separation, recommended prompts, and one-click communication options to enhance the usability wizard control of CARIS. This project offers a publicly available, context-adaptable tool for the HRI community, enabling researchers to streamline data-driven approaches to intelligent robot behavior.

cs.RO↗

arXiv2025

Human-Robot Teaming Field Deployments: A Comparison Between Verbal and Non-verbal Communication

Healthcare workers (HCWs) encounter challenges in hospitals, such as retrieving medical supplies quickly from crash carts, which could potentially result in medical errors and delays in patient care. Robotic crash carts (RCCs) have shown promise in assisting healthcare teams during medical tasks through guided object searches and task reminders. Limited exploration has been done to determine what communication modalities are most effective and least disruptive to patient care in real-world settings. To address this gap, we conducted a between-subjects experiment comparing the RCC's verbal and non-verbal communication of object search with a standard crash cart in resuscitation scenarios to understand the impact of robot communication on workload and attitudes toward using robots in the workplace. Our findings indicate that verbal communication significantly reduced mental demand and effort compared to visual cues and with a traditional crash cart. Although frustration levels were slightly higher during collaborations with the robot compared to a traditional cart, these research insights provide valuable implications for human-robot teamwork in high-stakes environments.

cs.RO↗

arXiv2025

Help or Hindrance: Understanding the Impact of Robot Communication in Action Teams

The human-robot interaction (HRI) field has recognized the importance of enabling robots to interact with teams. Human teams rely on effective communication for successful collaboration in time-sensitive environments. Robots can play a role in enhancing team coordination through real-time assistance. Despite significant progress in human-robot teaming research, there remains an essential gap in how robots can effectively communicate with action teams using multimodal interaction cues in time-sensitive environments. This study addresses this knowledge gap in an experimental in-lab study to investigate how multimodal robot communication in action teams affects workload and human perception of robots. We explore team collaboration in a medical training scenario where a robotic crash cart (RCC) provides verbal and non-verbal cues to help users remember to perform iterative tasks and search for supplies. Our findings show that verbal cues for object search tasks and visual cues for task reminders reduce team workload and increase perceived ease of use and perceived usefulness more effectively than a robot with no feedback. Our work contributes to multimodal interaction research in the HRI field, highlighting the need for more human-robot teaming research to understand best practices for integrating collaborative robots in time-sensitive environments such as in hospitals, search and rescue, and manufacturing applications.

cs.HC↗

arXiv2025

From Virtual Agents to Robot Teams: A Multi-Robot Framework Evaluation in High-Stakes Healthcare Context

Advancements in generative models have enabled multi-agent systems (MAS) to perform complex virtual tasks such as writing and code generation, which do not generalize well to physical multi-agent robotic teams. Current frameworks often treat agents as conceptual task executors rather than physically embodied entities, and overlook critical real-world constraints such as spatial context, robotic capabilities (e.g., sensing and navigation). To probe this gap, we reconfigure and stress-test a hierarchical multi-agent robotic team built on the CrewAI framework in a simulated emergency department onboarding scenario. We identify five persistent failure modes: role misalignment; tool access violations; lack of in-time handling of failure reports; noncompliance with prescribed workflows; bypassing or false reporting of task completion. Based on this analysis, we propose three design guidelines emphasizing process transparency, proactive failure recovery, and contextual grounding. Our work informs the development of more resilient and robust multi-agent robotic systems (MARS), including opportunities to extend virtual multi-agent frameworks to the real world.

cs.RO↗

arXiv2025

Rapidly Built Medical Crash Cart! Lessons Learned and Impacts on High-Stakes Team Collaboration in the Emergency Room

Designing robots to support high-stakes teamwork in emergency settings presents unique challenges, including seamless integration into fast-paced environments, facilitating effective communication among team members, and adapting to rapidly changing situations. While teleoperated robots have been successfully used in high-stakes domains such as firefighting and space exploration, autonomous robots that aid highs-takes teamwork remain underexplored. To address this gap, we conducted a rapid prototyping process to develop a series of seemingly autonomous robot designed to assist clinical teams in the Emergency Room. We transformed a standard crash cart--which stores medical equipment and emergency supplies into a medical robotic crash cart (MCCR). The MCCR was evaluated through field deployments to assess its impact on team workload and usability, identified taxonomies of failure, and refined the MCCR in collaboration with healthcare professionals. Our work advances the understanding of robot design for high-stakes, time-sensitive settings, providing insights into useful MCCR capabilities and considerations for effective human-robot collaboration. By publicly disseminating our MCCR tutorial, we hope to encourage HRI researchers to explore the design of robots for high-stakes teamwork.

cs.RO↗