In the ever-evolving era of technological advancement, the intersection of art and robotics has arisen as a captivating frontier, where the precision of mechanisms is blended with the emotive impact of human expression. Although dance and robotics seem to be two different disciplines, they share a common study field: movement. The purpose of dance, and generally for theatrical discipline, movement research is to investigate how to create a poetic language through human body movements and choreographed gestures that captivate audiences. On the other hand, conventional research about robotics focuses on the programming or learning of kinematic chains to efficiently achieve pragmatic tasks. In the context of this study, we aim to bridge the gap between these seemingly different application fields by introducing a novel approach to generate aesthetic choreographies for a robotic arm and study the perception shift from pragmatic and industrial-like to artistic and aesthetic in the audience opinion. The proposed method consists of the decomposition of robot and dancer movements into their Principal Components (PC) to fill the gap between the amount of degree of freedom (DoF) of the human kinematic structure and the robot one, minimizing the nuance loss due to the dimensionality reduction. Subsequently, the PCs of inherent movements of these two different kinematic structures are merged and blended. This approach allows for the harmonious fusion of the precision-driven motions of the robotic arm with the organic, emotive gestures intrinsic to dance. This synergistic blending of elements not only enhances the aesthetic appeal of the robotic arm’s but also introduces a captivating narrative dimension to its industrial functionality.
Amidst the mounting challenges posed by the ageing of worldwide populations, innovative care approaches need to be explored. Socially Assistive Robots (SARs) have emerged as a potential solution, however despite their potential benefits, they have yet to realise widespread utilisation. We conducted a mixed-methodology study to explore the perspectives of 24 support workers and other healthcare professionals on SARs and the potential integration of “needy” traits to increase acceptance of future SARs. Needy traits in this context refers to the integration of traits which produce a sense of dependence from the robot to the user, with the intention of fostering similar engagements as between a person and their pet. Focus groups, questionnaires and interviews were utilised to gather valuable insights. Overall, support workers exhibited a positive attitude towards existing SARs and were receptive to the concept of an SAR with “needy” traits. However, several significant barriers including privacy, technology literacy, and cost still need to be addressed to allow for their widespread adoption.
Robotic technologies are often proposed to relieve dull, dirty, or dangerous work, but may cause work to instead be experienced as boring, or dehumanized. Understanding the impact of robotics on the workfloor is complicated by the entanglement between emerging robotic capabilities, social dynamics, and organizational issues—which we call Worker–Robot Relations. Consequently, the impact of robotics on work is often studied in hindsight. Speculative design methodologies can facilitate alignment of robotic developments with a meaningful future of work, by creating boundary objects for communicating about current and future work practices. Making use of an unfolding artistic collaboration, we propose an experiential approach for speculating about future Worker–Robot Relations. We enabled speculative encounters between participants and robotic creatures that embody seven meta-behaviors. We abstracted these from observed behaviors in a current work context in baggage handling. We present the findings from focus groups responding to these encounters, including implications for HRI and speculative design.
Despite the rise of mobile robot deployments in community settings, the perceived safety of cohabitants remains understudied in many domains. To address this gap, we perform a study to identify elements of indoor human–mobile robot encounters that impact perceived safety. This study evaluates the effects of robot movement behavior and the number of robots nearby on perceived safety of participants. Further, this article investigates how the presence of other people impacts perceived safety in such settings. We leverage methodologies from physiological signal analysis, autonomy, surveys, and qualitative interviews to decode insights into the human experience during such encounters. Particularly, signal analysis yielded that the presence of multiple robots decreases perceived safety and that search behaviors were more comfortable than navigation behaviors. Similarly, interviews with participants demonstrated clear effects on perceived safety in the presence of others, and that sensemaking was a key component involved in their perceptions of safety. When the data were combined, interview data revealed that near collisions between the robots likely confounded the signal analysis findings with respect to the number of robots and their movement behavior. The data types agree that the presence of a robot impacts perceived safety; however, there are also conflicting results that we discuss, which highlight that near-collisions impact perceived safety. In aggregate, the study illustrates the benefits of leveraging eclectic methods to ascertain deeper insights. Overall, the article aims to unlock insights into human perceptions during encounters with community embedded robots, which can be used in the future design of such systems.
We introduce a novel framework with strong generalization capabilities for enabling humanoid robots to perform semantically grounded, context-aware, and physically realizable expressive behaviors, with the goal of enhancing end-user engagement in open-world human–robot interaction (HRI). To achieve this goal, we develop a new large language model–based human cognition module that interprets user dialogs to infer latent intent and generates high-level multi-modal behavior descriptions. These semantic representations are mapped to speech and motion outputs through a dual-stage embodied behavior generation pipeline. The pipeline consists of a shape adaptation module that maps human body motions into the robot’s kinematic space, followed by a motion retargeting module that generates executable joint trajectories under physical constraints. Additionally, the modular architecture enables seamless integration of state-of-the-art generative models and serves as a practical testbed for evaluating expressive behavior generation in real-world settings. We validate this system on a 58-DoF humanoid platform through both controlled video-based studies and live HRI experiments. The results show significant improvements over rule-based and handcrafted baselines in terms of expressiveness, behavioral appeal, and user engagement. This work helps bridging the gap between high-level language understanding and low-level robot control, thereby enabling scalable and human-aligned expressive behavior generation for embodied agents.
In this article, we investigate using explanations to improve decision-making for robotic scientific data collection missions. We propose the Preference Elicitation with contrasting Feature-based eXplanations (PrEFeX) method, which combines preferences with contrasting explanations focused on a single explanatory feature. We first provide a verification of the contrasting explanations by themselves using a planner prediction user study with 16 expert participants (Phase 1). This study showed that there was no increase in understanding of robot plans using contrasting explanations without preferences. To elucidate what information the autonomous measurement selection system was missing to be useful, we interviewed 4 planetary scientists and 2 oceanographers (Phase 2). We found that scientists focused heavily on understanding the objectives of the system and wanted explanations that (1) grounded the explanations to tradeoffs the system made, (2) connected the explanations to an ability to modify the behavior of the decision making, and (3) attached the explanation system’s features to comparable features the scientists considered. To this end, we propose combining explanations with user preference learning of the reward function in an iterative design process of the measurement plans with scientists in the loop. We tested our proposed preference and explanation system in a field deployment with planetary scientists on Mt. Hood, Oregon, and performed a post-data collection survey on the quality of the plans with 22 experts (Phase 3). We found the experts preferred the measurement plan selected by our proposed PrEFeX method over a baseline without explanations or preferences.
This study investigates how different forms of operator embodiment (avatar robots, telepresence robots, and direct in-person interaction) affect people’s willingness and ability to approach and initiate social interactions with strangers in public spaces. Participants were tasked with distributing information to pedestrians in a shopping mall across the three embodiment conditions. We examined behavioral performance (e.g., frequency of approaches, hesitation time), self-reported anxiety, fear of talking to strangers, and post-interaction reflections. Results showed that participants using robot-mediated embodiment (avatar and telepresence robots) exhibited greater social engagement and reported less hesitation time, lower state anxiety, and reduced fear of talking to strangers compared to the in-person condition. Notably, the avatar robot condition was associated with a greater reduction in fear of talking to strangers than the telepresence condition. Our interview results suggest that the reduction may be attributed to the anonymity provided by the avatar, which decreases self-consciousness and social pressure. Additionally, many participants noted that pedestrians showed heightened interest in the robots, which made initiating interactions easier. Telepresence robots also offered psychological relief by removing the need for physical presence, although some participants were more self-aware due to their visible face and voice. While most participants reported feeling nervous in face-to-face interactions, some appreciated the physical freedom and focused delivery enabled by in-person engagement. These findings suggest that teleoperated robots can mitigate psychological barriers in socially demanding tasks, and that embodiment plays a role in shaping operator experience and social outcomes.
The increasing integration of humanoid robots into educational and social contexts underscores the importance of understanding how young children perceive robots. This study examines how children aged 3–6 years (N = 90, 54 females) and adults (N = 24, 11 females) attribute physical, biological, and mental properties to humanoid robots, assessing the impact of humanoid appearance on anthropomorphic perceptions. Participants engaged in a structured card-choice task featuring stimuli varying in humanoid characteristics, including an intelligent phone and robots with differing degrees of humanoid features. Results revealed that while children predominantly recognized robots as “non-living,” their attributions were significantly influenced by anthropomorphic features, particularly eyes and limbs. Younger children (3–5 years) demonstrated higher levels of anthropomorphism compared to older children and adults, frequently attributing biological and mental properties to robots with pronounced human-like features. These findings advance previous research by documenting the developmental trajectory of anthropomorphic perceptions from early preschool (ages 3–6) to adulthood. In contrast to prior literature, our study manipulates humanoid appearances along a spectrum (from non-humanoid devices to fully humanoid robots), identifying expressive eyes and articulated limbs as distinct visual features that disproportionately drive young children’s anthropomorphic attributions. This developmental perspective and systematic stimulus comparison provide novel insights with direct implications for designing educational robots tailored for young children. Future research should explore interactive stimuli to further understand the detailed effects of humanoid robotics on child development.
To investigate the use of different pointing forms in service scenarios, we collected the ShopPoint dataset, a skeleton-based dataset of pointing gestures from customer–shopkeeper interactions in a camera shop scenario. Thirteen participants took part in the data collection, including 3 shopkeepers with real-world customer service experience and 10 customers. We recorded 61 one-to-one role-played interactions. Coders annotated pointing gestures from videos of these interactions, emphasizing pointing arm forms (straight-arm, bent-arm, and hand-only pointing) and hand forms (index-finger and open-hand pointing). This annotation process resulted in 2,959 pointing gestures. We conducted statistical analysis on the annotated data. The analysis revealed that bent-arm pointing was used more frequently than other arm forms. Straight-arm pointing was used more for far targets than for close targets, and hand-only was used more for close targets. Shopkeepers used bent-arm pointing more frequently than customers when referring to far targets. To evaluate the recognition of these pointing gestures, we tested several existing Skeleton-based Action Recognition (SAR) methods on the dataset. The highest accuracy was achieved at 72.51% by using transfer learning (i.e., pretraining and fine-tuning). This evaluation indicates that though transfer learning aids performance, recognizing pointing with diverse forms remains challenging.
People with physical impairments often face difficulties in performing daily tasks such as dressing, leading to dependence on caregivers. While robotic manipulators can provide valuable assistance, close physical interaction raises concerns over safety, comfort and trust, which can limit adoption. This article introduces the Assistive Robot Twin (ART) framework, a real-time digital twin that models both the user and the robot to enhance transparency during robot-assisted dressing. ART integrates two visual safety features: Bounding Boxes (BBs), which define static or dynamic protective zones around critical regions, and Trajectory Visualisation (TV), which displays planned robot movements in real time. We conducted a within-subject study with 36 participants mimicking stroke-related mobility impairment, evaluating six BB/TV configurations using validated interaction quality and system usability questionnaires. The results show that BBs significantly improved perceived safety ( \(\textrm{p} < 0.001\) ), reduced discomfort ( \(\textrm{p} < 0.001\) ) and increased trust ( \(\textrm{p} < 0.001\) ), with dynamic BBs providing the greatest safety benefits. TV significantly enhanced overall system usability ( \(\textrm{p}=0.007\) ), confidence and predictability of robot actions. While the study focuses on perceived interaction quality in a controlled setting with healthy participants, the results provide foundational evidence for the design of transparent assistive systems prior to clinical deployment.
As a young and inherently interdisciplinary field, Human–Robot Interaction (HRI) shows evidence of multiple, competing and complementary epistemologies and methodologies. However, it is clear that HRI user studies are a valued, primary form of knowledge generation. How might this influence, and be influenced by choice of experimental robot platform? Once a clear, common platform of choice, the number of HRI conference papers detailing work utilising the NAO robot has consistently declined since 2015. We take this opportunity to present quantitative and qualitative data regarding the evolving use and role of the NAO robot in establishing what makes ‘good science’ in HRI, and to reflect on what this might mean for, and can tell us about, the field more broadly. We suggest that shared use of a common platform like NAO is emblematic of, and important for, the field’s attempts to stabilise. Access to a common platform supports sharing of knowledge and practices amongst researchers in the field, whilst at the same time produces a set of assumptions about what makes for (seemingly) scientific HRI knowledge, practice or ‘lore’ that will continue to shape the future of the field—even beyond the platform’s demise.
Abstract: We propose that social robots able to interact in a “bidirectionally physical” manner, by continuously modulating their own movement and impedance in a direct coupling with the user's rather than simply triggering pre-set physical responses, have high potential for social expressiveness and durable engagement. This study explores how the dynamics of robot bunting, a feline behavior involving playful, affective force exchanges, can influence user perception of robot Personality Attributes ( e.g., extroversion and openness) and Interaction Qualities ( e.g., intentionality, liveliness, aliveness). With a minimalist one-degree-of-freedom (1-DF) haptic display, we created a control architecture to portray an abstract zoomorphic creature and conducted a user study (N=14) to evaluate the effects of Robot Role ( Leader or Follower ) and three Physical Dynamics variables (rendered stiffness, motion frequency and time delay). Participants stroked, scratched and petted the robot under each robot condition and reported on their social perceptions of it. Varying Robot Role significantly impacted attributes in both groups, while Physical Dynamics influence was concentrated on Interaction Qualities. Physical Dynamics factor variables behaved differently in each Role; e.g., when the robot was a Leader , high stiffness enhanced perceptions of intentionality and high motion frequency conveyed liveliness, while as a Follower , increased stiffness and delay raised user impressions of aliveness. This research demonstrates how physical qualities can shape human social perceptions of even a simple, abstract robot and that their subtle adjustment can create a wide range of social manifestations, with implications for generating sustained human-robot engagement.
As robots increasingly collaborate with humans in dynamic environments, the tension between communication clarity and social appropriateness presents an important design challenge. While communicative clarity is traditionally assumed to enhance human–robot interaction, recent findings suggest that clear communication can backfire when robots violate social norms. This study introduces and validates a “social amplifier” model of embodied communication, proposing that the value of role clarity depends on the appropriateness of the robot’s social behavior. We developed a novel confidence-aware role adaptation paradigm where an NAO robot dynamically switches between socially appropriate and inappropriate roles based on users’ confidence levels in an ambiguous video discrimination task. Using a within-subjects design with 64 participants, we manipulated role complementarity, choice alignment, and embodied communication across 512 total interactions. Results revealed that embodied communication significantly enhanced role clarity but had conditional effects on collaborative outcomes. Contrary to the “bad is stronger than good” principle, embodied communication asymmetrically amplified positive effects of appropriate robot behavior while having minimal impact on inappropriate behavior. This pattern emerged consistently across subjective measures (collaboration quality, role dynamics, trust) and objective behavioral outcomes (confidence change, choice change). The findings also demonstrate that robots with socially appropriate roles became over 7 times more persuasive when enhanced with rich embodied cues, while inappropriate robots showed no benefit. These results provide evidence-based design principles prioritizing social intelligence over mere expressiveness in collaborative decision-making robots.
Robot teammates could re-embody to enhance communication through gaze and gestures in distributed human-robot teaming (HRT) within virtual reality (VR) environments, offering potential benefits for time-critical domains such as emergency response. Yet despite this promise, robot re-embodiment remains underexplored in immersive settings and there is little clarity on how re-embodied robots should be visually designed. In this mixed-methods study, we used Immersive Speculative Enactments to investigate how people make sense of re-embodying robot teammates based on their visual forms in a shared VR environment. In pairs, 42 participants enacted three emergency scenarios with speculative robot teammates (a drone, humanoid firefighter, and fire truck) presented in machine-like, augmented, and human-like embodiments. Thematic analysis of post-session interviews, supported by quantitative data on participants’ VR experiences, showed that sense-making was shaped by robot type, task context, and social norms. Machinelike forms supported recognition of identity and function and were generally preferred; augmented forms blended social and functional cues; and humanlike forms prompted richer social interpretations but sometimes blurred identity and role boundaries. Participants’ future imaginaries of re-embodiment reflected concerns about contextual risk, automation reliability, and emotional labour. Together, these findings provide early insights into how identity, form, and function shape the interpretation of robot teammates in VR, offering guidance for the design of future re-embodiment systems and directions for further research.
The world’s population is aging, and there is a global drive toward developing a new care paradigm to support the older population. Robots have been seen as a unique solution to augment care because of their potential to support everyday living activities. We conducted a mixed-method scoping review of robots for older adults to establish the state of the science for robots supporting older adults. We explored the literature available on robots for older adults and identified key concepts and trends over 12 years (2010–2022). We included 205 studies following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The results revealed a growth in the range of studies evaluating robots for older adults. Key findings include characteristics of older adults involved in the research, the types of robots used, the tasks focused on, and the contexts for robot interaction and evaluation with older adults. This review provides valuable analysis and insights, offering research evidence for researchers, robot companies, and home care support personnel seeking to identify the potential of robots and advance design and application of robots to support older adults.
The increasing deployment of low-cost mobile robotic platforms offers transformative potential for addressing dull, dirty and dangerous tasks across sectors such as defence, nuclear, logistics and agriculture. Forecasts project the global multi-robot systems (MRS) market to exceed $5.9 billion by 2028, driven by demand for scalable, autonomous solutions. As mobile multi-robot systems (MMRS) transition from research prototypes to operational assets, the design of effective human–robot interaction (HRI) becomes a critical enabler of safe, efficient and mission-relevant deployment. Non-dyadic deployment, where the number of robots exceeds the number of human operators, is the natural extension to maximise benefit by delivering systems at increased scales. Thus, this article is the first systematic review to focus on the application of HRI paradigms exclusively to non-dyadic MMRS. The systematic literature review employed the PRISMA methodology, analysing 2,314 peer-reviewed papers sourced from ACM and IEEE repositories. This article identifies three persistent gaps in the MMRS interaction literature: (1) the limited treatment of manned-unmanned teaming (MUM-T) as an operational scenario and (2) the under-utilisation of consumer-off-the-shelf (COTS) wearable devices as viable interaction paradigms. Additionally, (3) the article introduces a structured reporting framework to enhance consistency, replicability and contextual richness in MMRS interaction studies, elevating the comparative and practical value of future work. Identified gaps, and the proposed reporting framework, are particularly salient in light of expanding global investment and the trajectory toward increasingly complex, cross-sector MMRS deployments. By supporting structured future research directions and enhancing reporting practices, this article contributes to advancing the state-of-the-art in MMRS interaction, fostering more impactful and practically relevant research outcomes. For researchers and practitioners alike, this review provides a foundation for designing scalable multi-robot interaction that meets the demands of tomorrow’s autonomous systems, where human oversight must remain effective, as robot numbers grow.
In this work, we examine the potential harms of social robotic surveillance and manipulation to robot users and society. We discuss these potential harms through the lenses of the sociological theories of surveillance capitalism as detailed by Zuboff and governmentality as proposed by Foucault. To map and address these risks, we contribute (1) an initial multi-level theoretical framework of harms on the micro-, meso- and macro-levels of society; (2) a mapping of how specific social robotic capabilities can induce harms via surveillance and manipulation; (3) the exemplification of potential harms to robot users and society through speculative human–robot interaction (HRI) scenarios grounded in previous HRI literature; and (4) recommendations on how the HRI community could begin addressing these harms. This article aims to demonstrate that while many user-aligned and helpful use cases have been explored for social robots, the current economic paradigm of surveillance capitalism and the aims of governmentality can overshadow these use cases. We argue that roboticists should think critically about the prevailing economic and political systems, which their robots will inhabit, consider how power is distributed in HRI, reflect on how the identified harms may be perpetuated by their research and critically consider how these harms should be addressed.
Although AI systems are becoming increasingly common in the workplace, research on their integration into human teams remains limited. In particular, little is known about how the embodiment of artificial agents shapes collaboration and performance in non-routine analytical tasks. To address this gap, we examine how different degrees of embodiment affect team performance and conversational dynamics in a real-life escape room. Teams composed of either three humans or two humans and an artificial agent (a Box, an Avatar, or a hyper-realistic humanoid) worked together to escape the room within a time limit. Our findings show that artificial agents have an uneven impact on team outcomes, with some mixed human–AI teams performing exceptionally well and others markedly worse. Human-only teams, by contrast, display more consistent performance: they are more likely to complete all tasks successfully, although they take longer and commit more errors. We also document a suggestive non-linear relationship between embodiment and team performance. Teams interacting with more embodied agents display conversational patterns that more closely resemble human–human dialogue. Together, these findings show that embodied AI shapes collaboration in complex ways, reinforcing evidence that social cues critically guide teamwork dynamics.
This study investigated dynamic structural changes and their implications for dynamic stability during Sit-to-Stand (STS) transitions with healthcare robot assistance. The STS movement, a fundamental activity for mobility and independence, poses challenges for older adults due to age-related declines in muscle strength and joint integrity, increasing fall risk. Assistive robots, typically categorized into upper- and lower-limb support systems, have been developed to mitigate this risk. However, conventional biomechanical assessments fail to capture the nonlinear, time-varying coordination patterns essential for maintaining dynamic stability in Human–Robot Interaction (HRI). To address this gap, we introduced nonlinear analytical techniques to detect dynamic structural changes during STS transitions under three experimental conditions: self-performed, passive robot-assisted, and proactive robot-assisted STS. Motion capture data from 14 healthy participants were analyzed, with segment synergies quantified through relative phase analysis of the trunk, knee, and ankle. A segment-synergy-based segmentation algorithm based on dynamic correlation exponents was developed to detect phase transitions in synergy dynamics. Complementary nonlinear-dynamics measures—maximum Lyapunov exponent, sample entropy, and detrended fluctuation analysis—were employed to assess dynamic stability. Results showed that passive assistance induced earlier but less synchronized transitions than self-performed STS, whereas proactive assistance preserved natural timing and enhanced intersegmental coordination stability. Temporal dissociation between knee–trunk and ankle–knee synergy transitions emerged as a potential predictor of dynamic stability outcomes. These findings underscore the importance of phase-sensitive control algorithms in assistive robotics for achieving biologically congruent HRI. By bridging biomechanical insights with advanced analytical methods, this study provides a critical step toward the design of healthcare robots that enhance mobility and reduce fall risk.
Advances in robotic autonomy and interfaces have transformed human–robot teaming across domains, from disaster response to planetary science. However, critical gaps remain in understanding how autonomy and interface design affect human performance and cognitive demands, especially in large-scale, unstructured environments such as those on the Moon or Mars. We present a human-in-the-loop system comprising two (semi-)autonomous robots supervised by a single human operator. The system was evaluated in real caves at Lava Beds National Monument (California) and in a controlled within-subject study (n = 38) at Polytechnique Montréal exploring both real and simulated caves. Participants interacted using either a traditional screen interface or a novel real-time, immersive VR interface, developed for this study and field-tested during NASA’s BRAILLE campaign. We find that continuous physiological measurements (HRV) align with subjective NASA Task Load Index (NASA TLX) scores in the context of human and multi-robot planetary exploration. Compared to benchmarks from prior studies, the screen interface resulted in low workload, while VR was rated in the low-to-moderate range. The low-autonomy VR-waypoint condition resulted in the least effective performance, with the fewest automated science detections, whereas both the full autonomy VR and screen-based conditions yielded comparably higher exploration and detection performance. Both interfaces supported high situational awareness, with accuracy measures near 90%. Autonomy did not significantly affect situational awareness, but full autonomy did reduce operator input effort. Trust levels did not significantly vary across conditions, motivating more detailed assessment methods in future studies. The results inform how to align interface design and autonomy to support effective multi-robot supervision in future missions.
