Social Robotics

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Social robotics designs and studies robots that communicate and work with people in settings such as homes, schools, healthcare, and public spaces. Systems combine speech, gaze, gesture, emotion recognition, memory, and increasingly large language or vision-language models to support assistance, learning, companionship, and socially aware navigation. Researchers use participatory design, controlled interaction studies, real-world deployments, and benchmarks to assess outcomes such as trust, safety, acceptance, and social appropriateness. A central challenge is making robots reliable and context-sensitive across diverse people and settings while balancing personalization and proactive help with privacy, user agency, and intrusiveness.

Latest papers

80 papers5 papers in the last four weeks, down 58% on the four weeks before. 0.0% of all new papers.

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 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 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

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 9, 2026

Age-Related Differences in the Perception of Eye-Gaze from a Social Robot

There is an increasing interest in social robots assisting older adults during daily life tasks. In this context, non-verbal cues such as deictic gaze are important in natural communication in human-robot interaction. However, the sensibility to deictic-gaze declines naturally with age and results in a reduction in social perception. Therefore, this work explores the benefits of deictic gaze from social robots assisting older adults during daily life tasks, and how age-related differences may influence their social perception in contrast to younger populations. This may help on the design of adaptive age-related non-verbal cues in the Human-Robot Interaction context.

Lucas Morillo-Mendez, Martien G. S. Schrooten, Oscar Martinez Mozos

Örebro University, Fakultetsgatan 1, 702 81, Örebro, Sweden

Human-Robot Interaction Social Robotics

Mar 9, 2026

POIROT: Investigating Direct Tangible vs. Digitally Mediated Interaction and Attitude Moderation in Multi-party Murder Mystery Games

As social robots take on increasingly complex roles like game masters (GMs) in multi-party games, the expectation that physicality universally enhances user experience remains debated. This study challenges the "one-size-fits-all" view of tangible interaction by identifying a critical boundary condition: users' Negative Attitudes towards Robots (NARS). In a between-subjects experiment (N = 67), a custom-built robot GM facilitated a multi-party murder mystery game (MMG) by delivering clues either through direct tangible interaction or a digitally mediated interface. Baseline multivariate analysis (MANOVA) showed no significant main effect of delivery modality, confirming that tangibility alone does not guarantee superior engagement. However, primary analysis using multilevel linear models (MLM) revealed a reliable moderation: participants high in NARS experienced markedly lower narrative immersion under tangible delivery, whereas those with low NARS scores showed no such decrement. Qualitative findings further illuminate this divergence: tangibility provides novelty and engagement for some but imposes excessive proxemic friction for anxious users, for whom the digital interface acts as a protective social buffer. These results advance a conditional model of HRI and emphasize the necessity for adaptive systems that can tailor interaction modalities to user predispositions.

Wen Chen, Rongxi Chen, Shankai Chen +5

Tsinghua University Beijing, China · University of Pennsylvania Philadelphia, USA · Syracuse University Syracuse, USA +2

Human-Robot Interaction Human-Object Interaction Social Robotics

Mar 9, 2026

Identifying Influential Actions in Human-Robot Interactions

Human-robot interaction combines robotics, cognitive science, and human factors to study collaborative systems. This paper introduces a method for identifying influential robot actions using transfer entropy, a statistic that measures directed information transfer between time series. TE is effective for capturing complex, nonlinear interactions. We apply this method to analyze how robot actions affect human behavior during a conversation with a remotely controlled robot avatar. By focusing on the impact of proximity, our approach demonstrates TE's capability to identify key actions influencing human responses, highlighting its potential to improve the design and adaptability of robotic systems.

Haoyang Jiang, Chenfei Xu, Yuya Okadome +1

Guardian Robot Project, R-IH, Riken, Kyoto, Japan · Faculty of Engineering, Monash University, Melbourne, Australia · Graduate school of engineering science, Osaka University, Osaka, Japan +1

Human-Robot Interaction Social Robotics

Mar 6, 2026

Material Driven HRI Design: Aesthetics as Explainability

Aesthetics - often treated as secondary to function-guides how people interpret robots' roles. A great deal of robot designs - both real and fictitious - use sleek industrial aesthetics. These feature hard glossy plastics, hiding as much of the underlying mechanical and electrical components as possible, resembling something akin to a nude humanoid figure. This leaves robots as something of a blank slate to which end-users apply coverings to, often based on media of fiction and non-fiction alike. We argue that designers can take cues from fashion to design interaction and set appropriate expectations. Rather than viewing appearance as decoration, we propose that color, texture, and material choices function as interaction signals. These signals can invite or discourage touch, clarify a robot's role, and help align user expectations with a robot's actual capabilities. When done thoughtfully, such cues can create familiarity and legibility; when done poorly, they can lead to wrong expectations. This preliminary paper proposes a framework describing how materials can create explainability by signaling expectations for interaction, task, and environment. We use this framework to do a content analysis of 6 robots.

Natalie Friedman, Kevin Weatherwax, Chengchao Zhu

BTP SAP Innovation Palo Alto, CA, USA

Human-Robot Interaction Social Robotics

Mar 5, 2026

NEFFY 2.0: A Breathing Companion Robot: User-Centered Design and Findings from a Study with Ukrainian Refugees

This paper presents the design of NEFFY 2.0, a social robot designed as a haptic slow-paced breathing companion for stress reduction, and reports findings from a mixed-methods user study with 14 refugees from Ukraine. Developed through a user-centered design process, NEFFY 2.0 builds on NEFFY 1.0 and integrates embodiment and multi-sensory interaction to provide low-threshold, accessible guidance of slow-paced breathing for stress relief, which may be particularly valuable for individuals experiencing prolonged periods of anxiety. To evaluate effectiveness, an experimental comparison of a robot-assisted breathing intervention versus an audio-only condition was conducted. Measures included subjective ratings and physiological indicators, such as heart rate (HR), heart rate variability (HRV) using RMSSD parameter, respiratory rate (RR), and galvanic skin response (GSR), alongside qualitative data from interviews exploring user experience and perceived support. Qualitative findings showed that NEFFY 2.0 was perceived as intuitive, calming and supportive. Survey results showed a substantially larger effect in significant reduction of perceived stress in the NEFFY 2.0 condition compared to audio-only. Physiological data reveled mixed results combined with large inter-personal variability. Three patterns of breathing practice with NEFFY 2.0 were identified using k-means clustering. Despite the small sample size, this study makes a novel contribution by providing empirical evidence of stress reduction in a vulnerable population through a direct comparison of robot-assisted and non-robot conditions. The findings position NEFFY 2.0 as a promising low-threshold tool that supports stress relief and contributes to the vision of HRI empowering society.

Ilona Buchem, Jessica Kazubski, Charly Goerke

Berlin University of Applied Sciences Berlin Germany

Human-Robot Interaction Social Robotics Mental Health Assistive Robotics

Mar 4, 2026

MistyPilot: An Agentic Fast-Slow Thinking LLM Framework for Misty Social Robots

With the availability of open APIs in social robots, it has become easier to customize general-purpose tools to meet users' needs. However, interpreting high-level user instructions, selecting and configuring appropriate tools, and executing them reliably remain challenging for users without programming experience. To address these challenges, we introduce MistyPilot, an agentic LLM-driven framework for autonomous tool selection, orchestration, and parameter configuration. MistyPilot comprises two core components: a Physically Interactive Agent (PIA) and a Socially Intelligent Agent (SIA). The PIA enables robust sensor-triggered and tool-driven task execution, while the SIA generates socially intelligent and emotionally aligned dialogue. MistyPilot further integrates a fast-slow thinking paradigm to capture user preferences, reduce latency, and improve task efficiency. To comprehensively evaluate MistyPilot, we contribute five benchmark datasets. Extensive experiments demonstrate the effectiveness of our framework in routing correctness, task completeness, fast-slow thinking retrieval efficiency, tool scalability,and emotion alignment. All code, datasets, and experimental videos will be made publicly available on the project webpage.

Xiao Wang, Lu Dong, Jingchen Sun +3

State University of New York at Buffalo, Buffalo, NY 14260, USA

Agentic AI for Robotics LLM Agent Orchestration Large Language Model-Based Robot Planning Robotic Tool Use Social Robotics Human-Robot Interaction

Mar 2, 2026

Co-designing a Social Robot for Newcomer Children's Cultural and Language Learning

Newcomer children face barriers in acquiring the host country's language and literacy programs are often constrained by limited staffing, mixed-proficiency cohorts, and short contact time. While Socially Assistive Robots (SARs) show promise in education, their use in these socio-emotionally sensitive settings remains underexplored. This research presents a co-design study with program tutors and coordinators, to explore the design space for a social robot, Maple. We contribute (1) a domain summary outlining four recurring challenges, (2) a discussion on cultural orientation and community belonging with robots, (3) an expert-grounded discussion of the perceived role of an SAR in cultural and language learning, and (4) preliminary design guidelines for integrating an SAR into a classroom. These expert-grounded insights lay the foundation for iterative design and evaluation with newcomer children and their families.

Neil Fernandes, Tehniyat Shahbaz, Emily Davies-Robinson +2

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

Social Robotics Assistive Robotics AI in Education Robotics Education

Feb 26, 2026

Designing Robots for Families: In-Situ Prototyping for Contextual Reminders on Family Routines

Robots are increasingly entering the daily lives of families, yet their successful integration into domestic life remains a challenge. We explore family routines as a critical entry point for understanding how robots might find a sustainable role in everyday family settings. Together with each of the ten families, we co-designed robot interactions and behaviors, and a plan for the robot to support their chosen routines, accounting for contextual factors such as timing, participants, locations, and the activities in the environment. We then designed, prototyped, and deployed a mobile social robot as a four-day, in-home user study. Families welcomed the robot's reminders, with parents especially appreciating the offloading of some reminding tasks. At the same time, interviews revealed tensions around timing, authority, and family dynamics, highlighting the complexity of integrating robots into households beyond the immediate task of reminders. Based on these insights, we offer design implications for robot-facilitated contextual reminders and discuss broader considerations for designing robots for family settings.

Michael F. Xu, Enhui Zhao, Yawen Zhang +3

Department of Computer Sciences, University of Wisconsin–Madison, Madison Wisconsin USA · Department of Computer Science, University of Illinois Chicago, Chicago Illinois USA · Department of Computer Science, University of Chicago, Chicago Illinois USA

Human-Robot Interaction Robotics Social Robotics

Feb 26, 2026

Relational Appliances: A Robot in the Refrigerator for Home-Based Health Promotion

Kitchen appliances are frequently used domestic artifacts situated at the point of everyday dietary decision making, making them a promising but underexplored site for health promotion. We explore the concept of relational appliances: everyday household devices designed as embodied social actors that engage users through ongoing, personalized interaction. We focus on the refrigerator, whose unique affordances, including a fixed, sensor-rich environment, private interaction space, and close coupling to food items, support contextualized, conversational engagement during snack choices. We present an initial exploration of this concept through a pilot study deploying an anthropomorphic robotic head inside a household refrigerator. In a home-lab apartment, participants repeatedly retrieved snacks during simulated TV "commercial breaks" while interacting with a human-sized robotic head. Participants were randomized to either a health-promotion condition, in which the robot made healthy snack recommendations, or a social-chat control condition. Outcomes included compliance with recommendations, nutritional quality of selected snacks, and psychosocial measures related to acceptance of the robot. Results suggest that participants found the robot persuasive, socially engaging, and increasingly natural over time, often describing it as helpful, aware, and companionable. Most participants reported greater awareness of their snack decisions and expressed interest in having such a robot in their own home. We discuss implications for designing relational appliances that leverage anthropomorphism, trust, and long-term human-technology relationships for home-based health promotion.

Timothy Bickmore, Mehdi Arjmand, Yunus Terzioglu

Northeastern University

Human-Robot Interaction Robotics Social Robotics

Feb 25, 2026

Trust in Autonomous Human--Robot Collaboration: Effects of Responsive Interaction Policies

Trust plays a central role in human--robot collaboration, yet its formation is rarely examined under the constraints of fully autonomous interaction. This pilot study investigated how interaction policy influences trust during in-person collaboration with a social robot operating without Wizard-of-Oz control or scripted repair. Participants completed a multi-stage collaborative task with a mobile robot that autonomously managed spoken-language dialogue, affect inference, and task progression. Two interaction policies were compared: a responsive policy, in which the robot proactively adapted its dialogue and assistance based on inferred interaction state, and a neutral, reactive policy, in which the robot provided only direct, task-relevant responses when prompted. Responsive interaction was associated with significantly higher post-interaction trust under viable communication conditions, despite no reliable differences in overall task accuracy. Sensitivity analyses indicated that affective and experiential components of trust were more sensitive to communication breakdown than evaluative judgments of reliability, and that as language-mediated interaction degraded, the trust advantage associated with responsiveness attenuated and ratings became less clearly interpretable as calibrated evaluations of collaborative competence. These findings suggest that trust in autonomous human--robot interaction emerges from process-level interaction dynamics and operates within constraints imposed by communication viability, highlighting the importance of evaluating trust under real autonomy conditions when designing interactive robotic systems.

Shauna Heron, Meng Cheng Lau

School of Social Sciences, Laurentian University, Sudbury, ON, P3E 2C6 · School of Engineering and Computer Science, Laurentian University, Sudbury, ON, P3E 2C6

Trust in HRI Human-Robot Interaction Human-Robot Collaboration Social Robotics

Feb 23, 2026

Botson: An Accessible and Low-Cost Platform for Social Robotics Research

Trust remains a critical barrier to the effective integration of Artificial Intelligence (AI) into human-centric domains. Disembodied agents, such as voice assistants, often fail to establish trust due to their inability to convey non-verbal social cues. This paper introduces the architecture of Botson: an anthropomorphic social robot powered by a large language model (LLM). Botson was created as a low-cost and accessible platform for social robotics research.

Samuel Bellaire, Abdalmalek Abu-raddaha, Natalie Kim +3

University of Michigan-Dearborn

Social Robotics Human-Robot Interaction

Feb 22, 2026

A Checklist for Deploying Robots in Public: Articulating Tacit Knowledge in the HRI Community

Many of the challenges encountered in in-the-wild public deployments of robots remain undocumented despite sharing many common pitfalls. This creates a high barrier of entry and results in repetition of avoidable mistakes. To articulate the tacit knowledge in the HRI community, this paper presents a guideline in the form of a checklist to support researchers in preparing for robot deployments in public. Drawing on their own experience with public robot deployments, the research team collected essential topics to consider in public HRI research. These topics are represented as modular flip cards in a hierarchical table, structured into deployment phases and important domains. We interviewed six interdisciplinary researchers with expertise in public HRI and show how including community input refines the checklist. We further show the checklist in action in context of real public studies. Finally, we contribute the checklist as an open-source, customizable community resource that both collects joint expertise for continual evolution and is usable as a list, set of cards, and an interactive web tool.

Claire Liang, Franziska Babel, Hannah Pelikan +2

Massachusetts Institute of Technology Cambridge, Massachusetts, USA · Linköping University Linköping, Sweden · Yale University New Haven, Connecticut, USA +1

Human-Robot Interaction Social Robotics

Feb 17, 2026

Designing Social Robots with Ethical, User-Adaptive Explainability in the Era of Foundation Models

Foundation models are increasingly embedded in social robots, mediating not only what they say and do but also how they adapt to users over time. This shift renders traditional ``one-size-fits-all'' explanation strategies especially problematic: generic justifications are now wrapped around behaviour produced by models trained on vast, heterogeneous, and opaque datasets. We argue that ethical, user-adapted explainability must be treated as a core design objective for foundation-model-driven social robotics. We first identify open challenges around explainability and ethical concerns that arise when both adaptation and explanation are delegated to foundation models. Building on this analysis, we propose four recommendations for moving towards user-adapted, modality-aware, and co-designed explanation strategies grounded in smaller, fairer datasets. An illustrative use case of an LLM-driven socially assistive robot demonstrates how these recommendations might be instantiated in a sensitive, real-world domain.

Fethiye Irmak Dogan, Alva Markelius, Hatice Gunes

University of Cambridge Cambridge, United Kingdom

Explainable Artificial Intelligence Social Robotics Human-Robot Interaction Robot Foundation Models

Feb 17, 2026

Robot-Assisted Social Dining as a White Glove Service

Robot-assisted feeding enables people with disabilities who require assistance eating to enjoy a meal independently and with dignity. However, existing systems have only been tested in-lab or in-home, leaving in-the-wild social dining contexts (e.g., restaurants) largely unexplored. Designing a robot for such contexts presents unique challenges, such as dynamic and unsupervised dining environments that a robot needs to account for and respond to. Through speculative participatory design with people with disabilities, supported by semi-structured interviews and a custom AI-based visual storyboarding tool, we uncovered ideal scenarios for in-the-wild social dining. Our key insight suggests that such systems should: embody the principles of a white glove service where the robot (1) supports multimodal inputs and unobtrusive outputs; (2) has contextually sensitive social behavior and prioritizes the user; (3) has expanded roles beyond feeding; (4) adapts to other relationships at the dining table. Our work has implications for in-the-wild and group contexts of robot-assisted feeding.

Atharva S Kashyap, Ugne Aleksandra Morkute, Patricia Alves-Oliveira

Robotics University of Michigan Ann Arbor, Michigan, USA · Leiden University Leiden, Netherlands

Assistive Robotics Human-Robot Interaction Social Robotics