Human-Robot Interaction

Also known as HRI

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Human-robot interaction examines how people and robots understand one another and coordinate in settings such as collaborative work, social navigation, healthcare, and the home. Researchers combine multimodal perception and language models with shared-control policies, adaptive planning, haptics, and interfaces such as augmented reality; they study these systems through user experiments, field deployments, and benchmarks for tasks like handover and social navigation. The field also draws on design research and qualitative methods to understand trust, accessibility, privacy, agency, and how interactions affect different people and groups. A central challenge is translating strong task performance in controlled tests into safe, reliable, and socially appropriate behavior in real-world settings without increasing workload or reducing human control.

Latest papers

453 papers44 papers in the last four weeks, down 31% on the four weeks before. 0.3% of all new papers.

Mar 30, 2026

Proposing a Game Theory Approach to Explore Group Dynamics with Social Robot

Integrating social robots in our group-based society, beyond the technical challenges, requires considering the social group dynamics. Following the results from preliminary exploratory studies on the influence of social robots on group decisions, the proposed research investigates whether social robots can foster cooperation among group members. To achieve this, I propose a game theory approach, employing the Public Good Game to recreate a simplified and controlled social situation where the robot's influence can be evaluated. Clarifying the role of robots in promoting collaboration among humans might have a significant impact in educational environments, enhancing student learning, as well as in workplace settings, where they could facilitate problem-solving and lead to shared solutions.

Giulia Pusceddu

Italian Institute of Technology, Genoa, Italy

Human-Robot Interaction Social Robotics Human-Robot Collaboration

Mar 30, 2026

Users and Wizards in Conversations: How WoZ Interface Choices Define Human-Robot Interactions

In this paper, we investigated how the choice of a Wizard-of-Oz (WoZ) interface affects communication with a robot from both the user's and the wizard's perspective. In a conversational setting, we used three WoZ interfaces with varying levels of dialogue input and output restrictions: a) a restricted perception GUI that showed fixed-view video and ASR transcripts and let the wizard trigger pre-scripted utterances and gestures; b) an unrestricted perception GUI that added real-time audio from the participant and the robot c) a VR telepresence interface that streamed immersive stereo video and audio to the wizard and forwarded the wizard's spontaneous speech, gaze and facial expressions to the robot. We found that the interaction mediated by the VR interface was preferred by users in terms of robot features and perceived social presence. For the wizards, the VR condition turned out to be the most demanding but elicited a higher social connection with the users. VR interface also induced the most connected interaction in terms of inter-speaker gaps and overlaps, while Restricted GUI induced the least connected flow and the largest silences. Given these results, we argue for more WoZ studies using telepresence interfaces. These studies better reflect the robots of tomorrow and offer a promising path to automation based on naturalistic contextualized verbal and non-verbal behavioral data.

Ekaterina Torubarova, Jura Miniota, Andre Pereira

Division of Speech, Music and Hearing, KTH Royal Institute of Technology, Stockholm, Sweden

Human-Robot Interaction Human-AI Interaction Human-Computer Interaction

Mar 30, 2026

Point of View: How Perspective Affects Perceived Robot Sociability

Ensuring that robot navigation is safe and socially acceptable is crucial for comfortable human-robot interaction in shared environments. However, existing validation methods often rely on a bird's-eye (allocentric) perspective, which fails to capture the subjective first-person experience of pedestrians encountering robots in the real world. In this paper, we address the perceptual gap between allocentric validation and egocentric experience by investigating how different perspectives affect the perceived sociability and disturbance of robot trajectories. Our approach uses an immersive VR environment to evaluate identical robot trajectories across allocentric, egocentric-proximal, and egocentric-distal viewpoints in a user study. We perform this analysis for trajectories generated from two different navigation policies to understand if the observed differences are unique to a single type of trajectory or more generalizable. We further examine whether augmenting a trajectory with a head-nod gesture can bridge the perceptual gap and improve human comfort. Our experiments suggest that trajectories rated as sociable from an allocentric view may be perceived as significantly more disturbing when experienced from a first-person perspective in close proximity. Our results also demonstrate that while passing distance affects perceived disturbance, communicative social signaling, such as a head-nod, can effectively enhance the perceived sociability of the robot's behavior.

Subham Agrawal, Aftab Akhtar, Nils Dengler +1

Humanoid Robots Lab, University of Bonn, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence and the Center for Robotics, Bonn, Germany

Social Robot Navigation Human-Robot Interaction

Mar 30, 2026

Control Without Control: Defining Implicit Interaction Paradigms for Autonomous Assistive Robots

Assistive robotic systems have shown growing potential to improve the quality of life of those with disabilities. As researchers explore the automation of various caregiving tasks, considerations for how the technology can still preserve the user's sense of control become paramount to ensuring that robotic systems are aligned with fundamental user needs and motivations. In this work, we present two previously developed systems as design cases through which to explore an interaction paradigm that we call implicit control, where the behavior of an autonomous robot is modified based on users' natural behavioral cues, instead of some direct input. Our selected design cases, unlike systems in past work, specifically probe users' perception of the interaction. We find, from a new thematic analysis of qualitative feedback on both cases, that designing for effective implicit control enables both a reduction in perceived workload and the preservation of the users' sense of control through the system's intuitiveness and responsiveness, contextual awareness, and ability to adapt to preferences. We further derive a set of core guidelines for designers in deciding when and how to apply implicit interaction paradigms for their assistive applications.

Janavi Gupta, Kavya Puthuveetil, Dimitra Tsakona +3

Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA · Imperial College London, London, UK

Human-Robot Interaction Robotic Control Assistive Robotics

Mar 28, 2026

Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses

Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in open-world, safety-critical environments. As these systems gain autonomy and enter domains such as transportation, healthcare, and industrial or assistive robotics, ensuring their safety becomes both technically challenging and socially indispensable. Unlike digital AI systems, embodied agents must act under uncertain sensing, incomplete knowledge, and dynamic human-robot interactions, where failures can directly lead to physical harm. This survey provides a comprehensive and structured review of safety research in embodied AI, examining attacks and defenses across the full embodied pipeline, from perception and cognition to planning, action and interaction, and agentic system. We introduce a multi-level taxonomy that unifies fragmented lines of work and connects embodied-specific safety findings with broader advances in vision, language, and multimodal foundation models. Our review synthesizes insights from over 500 papers spanning adversarial, backdoor, jailbreak, and hardware-level attacks; attack detection, safe training and robust inference; and risk-aware human-agent interaction. This analysis reveals several overlooked challenges, including the fragility of multimodal perception fusion, the instability of planning under jailbreak attacks, and the trustworthiness of human-agent interaction in open-ended scenarios. By organizing the field into a coherent framework and identifying critical research gaps, this survey provides a roadmap for building embodied agents that are not only capable and autonomous but also safe, robust, and reliable in real-world deployment.

Xiao Li, Xiang Zheng, Yifeng Gao +35

Fudan University · City University of Hong Kong · Jilin University +10

Embodied AI AI Safety AI Agent Security Robot Safety Human-Robot Interaction

Mar 26, 2026

A Mentalistic Interface for Probing Folk-Psychological Attribution to Non-Humanoid Robots

This paper presents an experimental platform for studying intentional-state attribution toward a non-humanoid robot. The system combines a simulated robot, realistic task environments, and large language model-based explanatory layers that can express the same behavior in mentalistic, teleological, or mechanistic terms. By holding behavior constant while varying the explanatory frame, the platform provides a controlled way to investigate how language and framing shape the adoption of the intentional stance in robotics.

Giulio Pisaneschi, Pierpaolo Serio, Estelle Gerbier +3

Institute of Clinical Physiology National Research Council, Pisa, Italy. · Department of Information Engineering, University of Pisa, Pisa, Italy. · Delft University of Technology, Delft, Netherlands.

Human-Robot Interaction Anthropomorphism in AI Social Robotics

Mar 26, 2026

Visualizing Impedance Control in Augmented Reality for Teleoperation: Design and User Evaluation

Teleoperation for contact-rich manipulation remains challenging, especially when using low-cost, motion-only interfaces that provide no haptic feedback. Virtual reality controllers enable intuitive motion control but do not allow operators to directly perceive or regulate contact forces, limiting task performance. To address this, we propose an augmented reality (AR) visualization of the impedance controller's target pose and its displacement from each robot end effector. This visualization conveys the forces generated by the controller, providing operators with intuitive, real-time feedback without expensive haptic hardware. We evaluate the design in a dual-arm manipulation study with 17 participants who repeatedly reposition a box with and without the AR visualization. Results show that AR visualization reduces completion time by 24% for force-critical lifting tasks, with no significant effect on sliding tasks where precise force control is less critical. These findings indicate that making the impedance target visible through AR is a viable approach to improve human-robot interaction for contact-rich teleoperation.

Gijs van den Brandt, Femke van Beek, Elena Torta

Department of Mechanical Engineering, Eindhoven University of Technology, The Netherlands

Bimanual Robotic Manipulation Contact-Rich Robotic Manipulation Robot Teleoperation Impedance Control Human-Robot Interaction

Mar 26, 2026

System Design for Maintaining Internal State Consistency in Long-Horizon Robotic Tabletop Games

Long-horizon tabletop games pose a distinct systems challenge for robotics: small perceptual or execution errors can invalidate accumulated task state, propagate across decision-making modules, and ultimately derail interaction. This paper studies how to maintain internal state consistency in turn-based, multi-human robotic tabletop games through deliberate system design rather than isolated component improvement. Using Mahjong as a representative long-horizon setting, we present an integrated architecture that explicitly maintains perceptual, execution, and interaction state, partitions high-level semantic reasoning from time-critical perception and control, and incorporates verified action primitives with tactile-triggered recovery to prevent premature state corruption. We further introduce interaction-level monitoring mechanisms to detect turn violations and hidden-information breaches that threaten execution assumptions. Beyond demonstrating complete-game operation, we provide an empirical characterization of failure modes, recovery effectiveness, cross-module error propagation, and hardware-algorithm trade-offs observed during deployment. Our results show that explicit partitioning, monitored state transitions, and recovery mechanisms are critical for sustaining executable consistency over extended play, whereas monolithic or unverified pipelines lead to measurable degradation in end-to-end reliability. The proposed system serves as an empirical platform for studying system-level design principles in long-horizon, turn-based interaction.

Guangyu Zhao, Ceyao Zhang, Chengdong Ma +16

Peking University · PKU-PsiBot Joint Lab · Nanyang Technological University +1

Robotics Human-Robot Interaction Long-Horizon Robotic Manipulation

Mar 26, 2026

A Minimum-Energy Control Approach for Redundant Mobile Manipulators in Physical Human-Robot Interaction Applications

Research on mobile manipulation systems that physically interact with humans has expanded rapidly in recent years, opening the way to tasks which could not be performed using fixed-base manipulators. Within this context, developing suitable control methodologies is essential since mobile manipulators introduce additional degrees of freedom, making the design of control approaches more challenging and more prone to performance optimization. This paper proposes a control approach for a mobile manipulator, composed of a mobile base equipped with a robotic arm mounted on the top, with the objective of minimizing the overall kinetic energy stored in the whole-body mobile manipulator in physical human-robot interaction applications. The approach is experimentally tested with reference to a peg-in-hole task, and the results demonstrate that the proposed approach reduces the overall kinetic energy stored in the whole-body robotic system and improves the system performance compared with the benchmark method.

Davide Tebaldi, Niccolò Paradisi, Fabio Pini +1

The authors are with the “Enzo Ferrari” Department of Engineering, University of Modena and Reggio Emilia, via Pietro Vivarelli 10, 41125 Modena, Italy · The author is with the Department of Engineering for Innovation Medicine, University of Verona

Robotic Control Contact-Rich Robotic Manipulation Human-Robot Interaction

Mar 25, 2026

Aesthetics of Robot-Mediated Applied Drama: A Case Study on REMind

Social robots are increasingly used in education, but most applications cast them as tutors offering explanation-based instruction. We explore an alternative: Robot-Mediated Applied Drama (RMAD), in which robots function as life-like puppets in interactive dramatic experiences designed to support reflection and social-emotional learning. This paper presents REMind, an anti-bullying robot role-play game that helps children rehearse bystander intervention and peer support. We focus on a central design challenge in RMAD: how to make robot drama emotionally and aesthetically engaging despite the limited expressive capacities of current robotic platforms. Through the development of REMind, we show how performing arts expertise informed this process, and argue that the aesthetics of robot drama arise from the coordinated design of the wider experience, not from robot expressivity alone.

Elaheh Sanoubari, Alicia Pan, Keith Rebello +3

Systems Design Engineering, University of Waterloo · Electrical & Computer Engineering, University of Waterloo · Communication Arts, University of Waterloo

Human-Robot Interaction Social Robotics

Mar 24, 2026

A Multimodal Framework for Human-Multi-Agent Interaction

Human-robot interaction is increasingly moving toward multi-robot, socially grounded environments. Existing systems struggle to integrate multimodal perception, embodied expression, and coordinated decision-making in a unified framework. This limits natural and scalable interaction in shared physical spaces. We address this gap by introducing a multimodal framework for human-multi-agent interaction in which each robot operates as an autonomous cognitive agent with integrated multimodal perception and Large Language Model (LLM)-driven planning grounded in embodiment. At the team level, a centralized coordination mechanism regulates turn-taking and agent participation to prevent overlapping speech and conflicting actions. Implemented on two humanoid robots, our framework enables coherent multi-agent interaction through interaction policies that combine speech, gesture, gaze, and locomotion. Representative interaction runs demonstrate coordinated multimodal reasoning across agents and grounded embodied responses. Future work will focus on larger-scale user studies and deeper exploration of socially grounded multi-agent interaction dynamics.

Shaid Hasan, Breenice Lee, Sujan Sarker +1

University of Virginia

Human-Robot Interaction Human-AI Interaction Multimodal Agents Multi-Agent Coordination

Mar 24, 2026

Human vs. NAO: A Computational-Behavioral Framework for Quantifying Social Orienting in Autism and Typical Development

Responding to one's name is among the earliest-emerging social orienting behaviors and is one of the most prominent aspects in the detection of Autism Spectrum Disorder (ASD). Typically developing children exhibit near-reflexive orienting to their name, whereas children with ASD often demonstrate reduced frequency, increased latency, or atypical patterns of response. In this study, we examine differential responsiveness to quantify name-calling stimuli delivered by both human agents and NAO, a humanoid robot widely employed in socially assistive interventions for autism. The analysis focuses on multiple behavioral parameters, including eye contact, response latency, head and facial orientation shifts, and duration of sustained interest. Video-based computational methods were employed, incorporating face detection, eye region tracking, and spatio-temporal facial analysis, to obtain fine-grained measures of children's responses. By comparing neurotypical and neuroatypical groups under controlled human-robot conditions, this work aims to understand how the source and modality of social cues affect attentional dynamics in name-calling contexts. The findings advance both the theoretical understanding of social orienting deficits in autism and the applied development of robot-assisted assessment tools.

Vartika Narayani Srinet, Anirudha Bhattacharjee, Braj Bhushan +1

1,2,4Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, U.P, India. · Department of Humanities and Social Sciences, Indian Institute of Technology Kanpur, Kanpur, 208016, U.P, India.

Autism Spectrum Disorder Human-Robot Interaction Social Robotics

Mar 20, 2026

Sense4HRI: A ROS 2 HRI Framework for Physiological Sensor Integration and Synchronized Logging

Physiological signals are increasingly relevant to estimate the mental states of users in human-robot interaction (HRI), yet ROS 2-based HRI frameworks still lack reusable support to integrate such data streams in a standardized way. Therefore, we propose Sense4HRI, an adapted framework for human-robot interaction in ROS 2 that integrates physiological measurements and derived user-state indicators. The framework is designed to be extensible, allowing the integration of additional physiological sensors, their interpretation, and multimodal fusion to provide a robust assessment of the mental states of users. In addition, it introduces reusable interfaces for timestamped physiological time-series data and supports synchronized logging of physiological signals together with experiment context, enabling interoperable and traceable multimodal analysis within ROS 2-based HRI systems.

Manuel Scheibl, Julian Leichert, Sinem Görmez +1

Medical Assistance Systems, Medical School OWL, Bielefeld University, 33501 Bielefeld, Germany · Center for Cognitive Interaction Technology, CITEC from Bielefeld University, Germany

Human-Robot Interaction Robotics Robotic Data Collection

Mar 19, 2026

Introducing M: A Modular, Modifiable Social Robot

We present M, an open-source, low-cost tabletop social robot platform aimed at lowering barriers to social robotics research by making robots easier to reproduce, modify, and deploy in real-world settings. M combines a modular mechanical design, multimodal sensing, and expressive yet mechanically simple actuation architecture with a ROS2-native software package that cleanly separates perception, expression control, and data management. The platform includes a simulation environment with interface equivalence to hardware to support easy sim-to-real transfer of interaction behaviors. We demonstrate extensibility through additional sensing/actuation modules and provide example interaction templates for storytelling and two-way conversational coaching. Lastly, we report real-world use of M in interaction design and week-long in-home deployments, showing how M can serve as a practical platform for longitudinal, reproducible social robotics research.

Victor Nikhil Antony, Zhili Gong, Yoonjae Kim +1

Department of Computer Science, Johns Hopkins University, USA · Department of Mechanical Engineering, Rice University, USA

Social Robotics Modular Robotics Human-Robot Interaction

Mar 19, 2026

MERGE: Guided Vision-Language Models for Multi-Actor Event Reasoning and Grounding in Human-Robot Interaction

We introduce MERGE, a system for situational grounding of actors, objects, and events in dynamic human-robot group interactions. Effective collaboration in such settings requires consistent situational awareness, built on persistent representations of people and objects and an episodic abstraction of events. MERGE achieves this by uniquely identifying physical instances of actors (humans or robots) and objects and structuring them into actor-action-object relations, ensuring temporal consistency across interactions. Central to MERGE is the integration of Vision-Language Models (VLMs) guided with a perception pipeline: a lightweight streaming module continuously processes visual input to detect changes and selectively invokes the VLM only when necessary. This decoupled design preserves the reasoning power and zero-shot generalization of VLMs while improving efficiency, avoiding both the high monetary cost and the latency of frame-by-frame captioning that leads to fragmented and delayed outputs. To address the absence of suitable benchmarks for multi-actor collaboration, we introduce the GROUND dataset, which offers fine-grained situational annotations of multi-person and human-robot interactions. On this dataset, our approach improves the average grounding score by a factor of 2 compared to the performance of VLM-only baselines - including GPT-4o, GPT-5 and Gemini 2.5 Flash - while also reducing run-time by a factor of 4. The code and data are available at www.github.com/HRI-EU/merge.

Joerg Deigmoeller, Nakul Agarwal, Stephan Hasler +8

Honda Research Institute Europe, 63073 Offenbach, Germany · Honda Research Institute USA, San Jose, CA 95134, USA.

Vision-Language Grounding Human-Robot Interaction Efficient VLM Inference

Mar 19, 2026

"You've got a friend in me": Co-Designing a Peer Social Robot for Young Newcomers' Language and Cultural Learning

Community literacy programs supporting young newcomer children in Canada face limited staffing and scarce one-to-one time, which constrains personalized English and cultural learning support. This paper reports on a co-design study with United for Literacy tutors that informed Maple, a table-top, peer-like Socially Assistive Robot (SAR) designed as a practice partner within tutor-mediated sessions. From shadowing and co-design interviews, we derived newcomer-specific requirements and added them in an integrated prototype that uses short story-based activities, multi-modal scaffolding and embedded quizzes that support attention while producing tutor-actionable formative signals. We contribute system design implications for tutor-in-the-loop SARs supporting language socialization in community settings and outline directions for child-centered evaluation in authentic programs.

Neil Fernandes, Cheng Tang, Tehniyat Shahbaz +4

University of Waterloo, Waterloo, Canada · United for Literacy, Toronto, Ontario, Canada

Participatory Design Human-Robot Interaction Social Robotics Assistive Robotics AI in Education Human-AI Co-Creation

Mar 19, 2026

Empathetic Motion Generation for Humanoid Educational Robots via Reasoning-Guided Vision--Language--Motion Diffusion Architecture

This article suggests a reasoning-guided vision-language-motion diffusion framework (RG-VLMD) for generating instruction-aware co-speech gestures for humanoid robots in educational scenarios. The system integrates multi-modal affective estimation, pedagogical reasoning, and teaching-act-conditioned motion synthesis to enable adaptive and semantically consistent robot behavior. A gated mixture-of-experts model predicts Valence/Arousal from input text, visual, and acoustic features, which then mapped to discrete teaching-act categories through an affect-driven policy.These signals condition a diffusion-based motion generator using clip-level intent and frame-level instructional schedules via additive latent restriction with auxiliary action-group supervision. Compared to a baseline diffusion model, our proposed method produces more structured and distinctive motion patterns, as verified by motion statics and pairwise distance analysis. Generated motion sequences remain physically plausible and can be retargeted to a NAO robot for real-time execution. The results reveal that reasoning-guided instructional conditioning improves gesture controllability and pedagogical expressiveness in educational human-robot interaction.

Fuze Sun, Lingyu Li, Lekan Dai +1

Department of Computer Science and Engineering, University of Liverpool

Co-Speech Gesture Generation Human-Robot Interaction Diffusion Models for Motion Generation

Mar 17, 2026

Influence of Gripper Design on Human Demonstration Quality for Robot Learning

Opening sterile medical packaging is routine for healthcare workers but remains challenging for robots. Learning from demonstration enables robots to acquire manipulation skills directly from humans, and handheld gripper tools such as the Universal Manipulation Interface (UMI) offer a pathway for efficient data collection. However, the effectiveness of these tools depends heavily on their usability. We evaluated UMI in demonstrating a bandage opening task, a common manipulation task in hospital settings, by testing three conditions: distributed load grippers, concentrated load grippers, and bare hands. Eight participants performed timed trials, with task performance assessed by success rate, completion time, and damage, alongside perceived workload using the NASA-TLX questionnaire. Concentrated load grippers improved performance relative to distributed load grippers but remained substantially slower and less effective than hands. These results underscore the importance of ergonomic and mechanical refinements in handheld grippers to reduce user burden and improve demonstration quality, especially for applications in healthcare robotics.

Gina L. Georgadarellis, Natalija Beslic, Seonhun Lee +2

Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst, Amherst, MA 01003 USA · Elaine Marieb Center for Nursing and Engineering Innovation, University of Massachusetts Amherst, Amherst, MA 01003 USA · Center for Personalized Health Monitoring, Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA 01003

Healthcare Robotics Robotics Robotic Manipulation Learning from Demonstration Robot Imitation Learning Human-Robot Interaction

Mar 17, 2026

Designing for Disagreement: Front-End Guardrails for Assistance Allocation in LLM-Enabled Robots

LLM-enabled robots prioritizing scarce assistance in social settings face pluralistic values and LLM behavioral variability: reasonable people can disagree about who is helped first, while LLM-mediated interaction policies vary across prompts, contexts, and groups in ways that are difficult to anticipate or verify at contact point. Yet user-facing guardrails for real-time, multi-user assistance allocation remain under-specified. We propose bounded calibration with contestability, a procedural front-end pattern that (i) constrains prioritization to a governance-approved menu of admissible modes, (ii) keeps the active mode legible in interaction-relevant terms at the point of deferral, and (iii) provides an outcome-specific contest pathway without renegotiating the global rule. Treating pluralism and LLM uncertainty as standing conditions, the pattern avoids both silent defaults that hide implicit value skews and wide-open user-configurable "value settings" that shift burden under time pressure. We illustrate the pattern with a public-concourse robot vignette and outline an evaluation agenda centered on legibility, procedural legitimacy, and actionability, including risks of automation bias and uneven usability of contest channels.

Carmen Ng

Technical University of Munich, Germany

LLM Guardrails AI Agent Governance Human-Robot Interaction LLM Decision-Making

Mar 17, 2026

Faulty Coffees: Barriers to Adoption of an In-the-wild Robo-Barista

We set out to study whether task-based narratives could influence long-term engagement with a service robot. To do so, we deployed a Robo-Barista for five weeks in an over-50's housing complex in Stockton, England. Residents received a free daily coffee by interacting with a Furhat robot assigned to either a narrative or non-narrative dialogue condition. Despite designing for sustained engagement, repeat interaction was low, and we encountered curiosity trials without retention, technical breakdowns, accessibility barriers, and the social dynamics of a housing complex setting. Rather than treating these as peripheral issues, we foreground them in this paper. We reflect on the in-the-wild realities of our experiment and offer lessons for conducting longitudinal Human-Robot Interaction research when studies unravel in practice.

Bruce W. Wilson, David A. Robb, Mei Yii Lim +3

Heriot-Watt University School of Mathematical and Computer Sciences Edinburgh, UK · University of Edinburgh School Informatics Edinburgh, UK · CereProc Ltd. Edinburgh, UK

Service Robotics Human-Robot Interaction Social Robotics Robotics

Mar 16, 2026

Gaze-Aware Task Progression Detection Framework for Human-Robot Interaction Using RGB Cameras

In human-robot interaction (HRI), detecting a human's gaze helps robots interpret user attention and intent. However, most gaze detection approaches rely on specialized eye-tracking hardware, limiting deployment in everyday settings. Appearance-based gaze estimation methods remove this dependency by using standard RGB cameras, but their practicality in HRI remains underexplored. We present a calibration-free framework for detecting task progression when information is conveyed via integrated display interfaces. The framework uses only the robot's built-in monocular RGB camera (640x480 resolution) and state-of-the-art gaze estimation to monitor attention patterns. It leverages natural behavior, where users shift focus from task interfaces to the robot's face to signal task completion, formalized through three Areas of Interest (AOI): tablet, robot face, and elsewhere. Systematic parameter optimization identifies configurations that balance detection accuracy and interaction latency. We validate our framework in a "First Day at Work" scenario, comparing it to button-based interaction. Results show a task completion detection accuracy of 77.6%. Compared to button-based interaction, the proposed system exhibits slightly higher response latency but preserves information retention and significantly improves comfort, social presence, and perceived naturalness. Notably, most participants reported that they did not consciously use eye movements to guide the interaction, underscoring the intuitive role of gaze as a communicative cue. This work demonstrates the feasibility of intuitive, low-cost, RGB-only gaze-based HRI for natural and engaging interactions.

Linlin Cheng, Koen Hindriks, Artem V. Belopolsky

Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. · Department of Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Human-Robot Interaction

Mar 16, 2026

Pointing-Based Object Recognition

This paper presents a comprehensive pipeline for recognizing objects targeted by human pointing gestures using RGB images. As human-robot interaction moves toward more intuitive interfaces, the ability to identify targets of non-verbal communication becomes crucial. Our proposed system integrates several existing state-of-the-art methods, including object detection, body pose estimation, monocular depth estimation, and vision-language models. We evaluate the impact of 3D spatial information reconstructed from a single image and the utility of image captioning models in correcting classification errors. Experimental results on a custom dataset show that incorporating depth information significantly improves target identification, especially in complex scenes with overlapping objects. The modularity of the approach allows for deployment in environments where specialized depth sensors are unavailable.

Lukáš Hajdúch, Viktor Kocur

Comenius University, Bratislava, Slovakia

Human-Robot Interaction

Mar 15, 2026

Towards Equitable Robotic Furnishing Agents for Aging-in-Place: ADL-Grounded Design Exploration

In aging-in-place contexts, small difficulties in Activities of Daily Living (ADL) can accumulate, affecting well-being through fatigue, anxiety, reduced autonomy, and safety risks. This position paper argues that robotics for older adult wellbeing must move beyond "convenience features" and centre equity, justice, and responsibility. We conducted ADL-grounded semi-structured interviews with four adults in their 70s-80s, identifying recurrent challenges (finding/ organising items, taking medication, and transporting objects) and deriving requirements to reduce compounded cognitive-physical burden. Based on these insights, we propose an in-home robotic furnishing-agent concept leveraging computer vision and generative AI and LLMs for natural-language interaction, context-aware reminders, safe actuation, and user-centred transparency. We then report video-stimulated follow-up interviews with the same participants, highlighting preferences for confirmation before actuation, predictability, adjustable speed/autonomy, and multimodal feedback, as well as equity-related concerns. We conclude with open questions on evaluating and deploying equitable robotic wellbeing systems in real homes.

Hansoo Lee, Changhee Seo, Subin Park +1

Imperial College London, United Kingdom · KIST, Republic of Korea

Assistive Robotics Human-Robot Interaction Human-Centered AI

Mar 13, 2026

From Woofs to Words: Towards Intelligent Robotic Guide Dogs with Verbal Communication

Assistive robotics is an important subarea of robotics that focuses on the well-being of people with disabilities. A robotic guide dog is an assistive quadruped robot that helps visually impaired people in obstacle avoidance and navigation. Enabling language capabilities for robotic guide dogs goes beyond naively adding an existing dialog system onto a mobile robot. The novel challenges include grounding language in the dynamically changing environment and improving spatial awareness for the human handler. To address those challenges, we develop a novel dialog system for robotic guide dogs that uses LLMs to verbalize both navigational plans and scenes. The goal is to enable verbal communication for collaborative decision-making within the handler-robot team. In experiments, we conducted a human study to evaluate different verbalization strategies and a simulation study to assess the efficiency and accuracy in navigation tasks.

Yohei Hayamizu, David DeFazio, Hrudayangam Mehta +7

The State University of New York at Binghamton 4400 Vestal Pkwy E, Binghamton, NY 13902 USA

Human-Robot Interaction Assistive Robotics Large Language Model-Based Robot Planning

Mar 12, 2026

ELLA: Generative AI-Powered Social Robots for Early Language Development at Home

Early language development shapes children's later literacy and learning, yet many families have limited access to scalable, high-quality support at home. Recent advances in generative AI make it possible for social robots to move beyond scripted interactions and engage children in adaptive, conversational activities, but it remains unclear how to design such systems for pre-schoolers and how children engage with them over time in the home. We present ELLA (Early Language Learning Agent), an autonomous, generative AI-powered social robot that supports early language development through interactive storytelling, parent-selected language targets, and scaffolded dialogue. Using a multi-phased, human-centered process, we interviewed parents (n=7) and educators (n=5) and iteratively refined ELLA through twelve in-home design workshops. We then deployed ELLA with ten children for eight days. We report design insights from in-home workshops, characterize children's engagement and behaviors during deployment, and distill design implications for generative AI-powered social robots supporting early language learning at home.

Victor Nikhil Antony, Shiye Cao, Shuning Wang +1

Johns Hopkins University, USA

Human-Robot Interaction Social Robotics AI in Education Generative AI in Education Human-Centered AI

Mar 12, 2026

Bridging the Awareness Gap: Socially Mediated State Externalization for Transparent Distributed Home Robots

Distributed multi-robot systems for the home often require robots to operate out of the user's sight, creating a state awareness gap that can diminish trust and perceived transparency and control. This paper investigates whether real-time, socially mediated state externalization can bridge this gap without compromising task performance. We developed a system where a co-located social mediator robot (Pepper) externalizes the hidden execution states of an out-of-sight mobile manipulator (Stretch~3) for voice-driven object retrieval and delivery, where task-level states are synchronized and externalized through verbal updates and visual progress display. In a counterbalanced within-subject study (N=30), we compared a baseline of Autonomous Hidden Execution against Socially Mediated State Externalization. Our results show that externalization significantly increases user task-focused attention (from 15.8% to 84.6%, p<.001) and substantially improves perceived perspicuity, dependability, stimulation, and attractiveness (all p<.001). Furthermore, 83% of participants preferred the externalized condition, and this improvement in user experience was achieved without a statistically significant increase in end-to-end task completion time (p=.271). The results suggest that socially mediated state externalization is an effective architectural mechanism for designing more transparent and trustworthy distributed robot systems, ultimately enhancing user experience without sacrificing performance in distributed home robot deployments.

Wenzheng Zhao, Manideep Duggi, Fengpei Yuan

Faculty of Robotics Engineering Department, Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, USA

Human-Robot Interaction Multi-Robot Systems Social Robotics

Mar 12, 2026

Beyond Theory of Mind in Robotics

Theory of Mind, the capacity to explain and predict behavior by inferring hidden mental states, has become the dominant paradigm for social interaction in robotics. Yet ToM rests on three assumptions that poorly capture how most social interaction actually unfolds: that meaning travels inside-out from hidden states to observable behavior; that understanding requires detached inference rather than participation; and that the meaning of behavior is fixed and available to a passive observer. Drawing on ethnomethodology, conversation analysis, and participatory sense-making, I argue that social meaning is not decoded from behavior but produced through moment-to-moment coordination between agents. This interactional foundation has direct implications for robot design: shifting from internal state modeling toward policies for sustaining coordination, from observer-based inference toward active participation, and from fixed behavioral meaning toward meaning potential stabilized through response.

Malte F. Jung

Theory of Mind Social Robotics Human-Robot Interaction

Mar 12, 2026

HiSync: Spatio-Temporally Aligning Hand Motion from Wearable IMU and On-Robot Camera for Command Source Identification in Long-Range HRI

Long-range Human-Robot Interaction (HRI) remains underexplored. Within it, Command Source Identification (CSI) - determining who issued a command - is especially challenging due to multi-user and distance-induced sensor ambiguity. We introduce HiSync, an optical-inertial fusion framework that treats hand motion as binding cues by aligning robot-mounted camera optical flow with hand-worn IMU signals. We first elicit a user-defined (N=12) gesture set and collect a multimodal command gesture dataset (N=38) in long-range multi-user HRI scenarios. Next, HiSync extracts frequency-domain hand motion features from both camera and IMU data, and a learned CSINet denoises IMU readings, temporally aligns modalities, and performs distance-aware multi-window fusion to compute cross-modal similarity of subtle, natural gestures, enabling robust CSI. In three-person scenes up to 34m, HiSync achieves 92.32% CSI accuracy, outperforming the prior SOTA by 48.44%. HiSync is also validated on real-robot deployment. By making CSI reliable and natural, HiSync provides a practical primitive and design guidance for public-space HRI. https://github.com/OctopusWen/HiSync

Chengwen Zhang, Chun Yu, Borong Zhuang +9

Department of Computer Science and Technology, BNRist Tsinghua University Beijing, China · Department of Computer Science and Technology, BNRist, College of AI Tsinghua University Beijing, China · Key Laboratory of Pervasive Computing, Ministry of Education +4

Human-Robot Interaction Multimodal Sensor Fusion

Mar 12, 2026

From Pets to Robots: MojiKit as a Data-Informed Toolkit for Affective HRI Design

Designing affective behaviors for animal-inspired social robots often relies on intuition and personal experience, leading to fragmented outcomes. To provide more systematic guidance, we first coded and analyzed human-pet interaction videos, validated insights through literature and interviews, and created structured reference cards that map the design space of pet-inspired affective interactions. Building on this, we developed MojiKit, a toolkit combining reference cards, a zoomorphic robot prototype (MomoBot), and a behavior control studio. We evaluated MojiKit in co-creation workshops with 18 participants, finding that MojiKit helped them design 35 affective interaction patterns beyond their own pet experiences, while the code-free studio lowered the technical barrier and enhanced creative agency. Our contributions include the data-informed structured resource for pet-inspired affective HRI design, an integrated toolkit that bridges reference materials with hands-on prototyping, and empirical evidence showing how MojiKit empowers users to systematically create richer, more diverse affective robot behaviors.

Liwen He, Pingting Chen, Ziheng Tang +4

The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China · Tsinghua University, Beijing, China · Beijing Institute of Technology, Beijing, China +3

Human-Robot Interaction Social Robotics Robotics

Mar 11, 2026

A Causal Approach to Predicting and Improving Human Perceptions of Social Navigation Robots

As mobile robots are increasingly deployed in human environments, enabling them to predict how people perceive them is critical for socially adaptable navigation. Predicting perceptions is challenging for two main reasons: (1) HRI prediction models must learn from limited data, and (2) the obtained models must be interpretable to enable safe and effective interactions. Interpretability is particularly important when a robot is perceived as incompetent (e.g., when the robot suddenly stops or rotates away from the goal), as it allows the robot to explain its reasoning and identify controllable factors to improve performance, requiring causal rather than associative reasoning. To address these challenges, we propose a Causal Bayesian Network designed to predict how people perceive a mobile robot's competence and how they interpret its intent during navigation. Additionally, we introduce a novel method to improve perceived robot competence employing a combinatorial search, guided by the proposed causal model, to identify better navigation behaviors. Our method enhances interpretability and generates counterfactual robot motions while achieving comparable or superior predictive performance to state-of-the-art methods, reaching an F1-score of 0.78 and 0.75 for competence and intention on a binary scale. To further assess our method's ability to improve the perceived robot competence, we conducted an online evaluation in which users rated robot behaviors on a 5-point Likert scale. Our method statistically significantly increased the perceived competence of low-competent robot behavior by 83%.

Maximilian Diehl, Nathan Tsoi, Gustavo Chavez +2

Faculty of Electrical Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden. · The University of Texas at Austin, Austin, Texas, USA. · Yale University, New Haven, Connecticut, USA.

Human-Robot Interaction Social Robot Navigation Explainable Motion Planning Causal Interpretability