Search Results (931)

In the field of human–robot interaction (HRI), researchers have extensively examined the role of social robot characteristics and how these can influence human–robot relationships. In particular, the robot’s voice is one of the most studied aspects, with numerous studies focusing on specific features such as tone, frequency, pitch, and gender. The robot’s voice represents a powerful social signal, whose design can influence people’s affective evaluations and acceptance of robots. With regard to language, however, relatively few studies have investigated the role of a robot’s accent (native or foreign). This experimental study therefore explores the influence of native accent on trust in robots. The study was conducted on two different samples: 60 Italian participants and 37 Arabic participants. Participants listened to two robot presentations in their native language: one delivered with a native accent and the other with a foreign accent. After listening to both presentations, participants were asked to indicate which robot they trusted. The results showed a 77.3% preference for the robot speaking with a native accent, compared to 22.7% for the robot with foreign accent. These findings demonstrate that, regardless of the language (Italian or Arabic), accent significantly influences the choice to invest trust in the robot, supporting the similarity-attraction effect. Accent calibration thus emerges as a low-cost, high-impact parameter in socially assistive and commercial robotics. Since accent influences trust-based delegation, voice design should be strategically adapted in service, healthcare, education, and customer-facing contexts. Full article

Introduction: Late functional outcomes remain major determinants of quality of life after robot-assisted radical prostatectomy (RARP). Although several baseline and perioperative factors have been linked to postoperative stress urinary incontinence (SUI) and erectile dysfunction (ED), their cumulative effect remains incompletely characterized in large real-world cohorts. Materials and Methods: This retrospective single-center study included 862 consecutive patients undergoing RARP for localized prostate cancer. All endpoints were assessed at a fixed 12-month follow-up visit; therefore, a median follow-up beyond this predefined time point was not applicable. Outcomes were derived from patient-reported information documented during routine follow-up and comprised pad use, ED, and urethral anastomotic stricture. Age, body mass index (BMI), console time, estimated blood loss, and prostate weight were selected a priori based on clinical relevance and uniform availability and were analyzed using univariable and multivariable logistic regression. A simple exploratory composite risk score (0–5 points) was constructed by assigning one point for each predefined adverse factor. Results: At 12 months, 50.0% of patients were pad-free, 85.6% achieved social continence (0–1 pad/day), 14.5% had clinically significant incontinence (>1 pad/day), 71.5% had chart-documented ED, and 1.0% developed urethral anastomotic stricture. In multivariable analysis, age (OR 1.039, 95% CI 1.018–1.059) and prostate weight (OR 1.011, 95% CI 1.004–1.018) independently predicted SUI, while age was the only independent predictor of ED (OR 1.029, 95% CI 1.007–1.050). No predictor of stricture was identified. The composite score showed an exploratory dose–response association with SUI (OR 1.364 per point, 95% CI 1.208–1.541; AUC 0.597) and a weaker association with ED (OR 1.149, 95% CI 1.007–1.313; AUC 0.540). Conclusions: A simple composite score may provide pragmatic exploratory grouping of SUI risk after RARP, but discrimination is modest and interpretation is limited by non-validated outcome assessment and the absence of major confounders, including nerve-sparing status and baseline functional measures. Full article

Artificial Intelligence (AI) is rapidly changing the format, style and content of scientific publishing. Traditional reviews are likely to give way to more personalized, AI-generated literature surveys on the one hand and more innovative, perhaps even controversial hypothesis, opinion or essay-style contributions on the other. Original publications based on experimental data are still less affected even if AI teams up with robots. Eventually, science and scientific publishing are social activities and although the AI-driven tools and technologies at hand may accelerate and also refine scientific publishing, scientists, as always, are well equipped to adapt and to turn these challenges into new opportunities, for instance in handling, processing and illustrating experimental data. Full article

Artificial Intelligence (AI) and robotics constitute two major technological fields frequently integrated into education. Both of them provide advantages to educational settings, stemming from approaches integrating them at all educational levels. The emergence of generative Artificial Intelligence and the growing popularity of related tools have accelerated the integration of AI into education. An aspect of interest is to explore the combination of AI with robotics in education, aiming to benefit from the advantages of both technological schemes. This paper reviews work regarding the combination of generative Artificial Intelligence and robotics in K-12 and higher education. Scopus was used to search for relevant work. Fifty-four relevant papers were retrieved and analyzed after an exhaustive search. Trends in this combination are highlighted, taking into consideration learning, teaching, robot functionality and capabilities of generative AI tools, teaching subjects, sample size, and educational levels. Five main types of generative AI and robotics combinations are discerned. The overall combination benefits and challenges are analyzed. To the best of the authors’ knowledge, there is no other review discussing this subject in this specific context. Full article

Pedestrian trajectory prediction is a fundamental task in autonomous driving and mobile robotics, where accurate forecasting requires modeling of both social interactions and scene-related constraints. However, existing methods typically rely on a fixed interaction modeling strategy, which may be insufficient under heterogeneous crowd densities. To address this limitation, we propose DAGH-Net, a density-adaptive gated hybrid network for pedestrian trajectory prediction. Built upon an SR-LSTM (State Refinement for LSTM) backbone, the proposed framework integrates two complementary reasoning pathways: a data-driven social interaction branch and a hybrid knowledge graph branch that encodes structured relational priors among pedestrians, obstacles, and walkable regions. A local-density-conditioned gating mechanism is further introduced to adaptively fuse these features according to the surrounding crowd condition of each pedestrian. This design helps suppress redundant interaction cues in sparse settings while strengthening socially compliant and scene-consistent reasoning in dense or conflict-prone environments. Experimental results on the ETH (Eidgenössische Technische Hochschule Zürich) and UCY (University of Cyprus) benchmarks, evaluated using Mean Average Displacement (MAD) and Final Average Displacement (FAD), show that DAGH-Net improves the average MAD and FAD by 1.6% and 4.2%, respectively, compared with SR-LSTM. Ablation studies further support the complementary contributions of the hybrid knowledge graph and the density-adaptive gating mechanism. We also discuss the limitations of the current density formulation and benchmark scale, which suggest several directions for future improvement. Full article

Autonomous robots are increasingly integrated into social contexts, making affective human–robot interaction (HRI) critical for their effectiveness and acceptance. However, existing research remains dispersed across domains and techniques, lacking a unified framework to characterize core robotic capabilities. To address this gap, we adopt a capability-oriented perspective and conduct a comprehensive literature review, through which we propose a structured taxonomy of capabilities for robots in affective HRI. The taxonomy comprises five core dimensions: perception (recognizing human internal states), strategy (planning responses based on human states and context), expression (conveying robot lifelikeness and social presence), sustainability (maintaining effective and reliable operation over time), and ethics (ensuring behavior within ethical constraints). By organizing diverse research efforts into a structured framework, this taxonomy provides a systematic foundation for designing socially competent robots and guiding future research. Full article

As social robots are increasingly deployed in domains such as healthcare, education, and entertainment, there is growing demand for affective agents that can interpret users’ affective states and respond in contextually appropriate ways. Existing work has established strong foundations for emotion generation and appraisal, but the step that connects generated emotion to behavioral execution still relies heavily on model-specific rules or implicit links. We frame this issue as a Mechanism Gap and propose an Emotional BDI framework that introduces Frijda’s action tendency as an intermediate representation layer between the Affective Core and the Belief–Desire–Intention (BDI) Executor. Rather than mapping emotion directly to concrete behavior, the framework first transforms affective state into a directional action tendency and then lets BDI reasoning realize that tendency according to role and context. This creates an explicit emotion-to-behavior mediation structure through which the same emotion can be expressed differently across situations and roles. In an exploratory user evaluation with 26 participants, the proposed model received more favorable ratings than an Emotion-Driven Agent in satisfaction ($p=0.010$) and appropriateness ($p=0.002$). Compared with a Cooperative Agent, the proposed model showed a significant advantage only in satisfaction ($p=0.030$). These findings suggest that the proposed framework offers a useful architectural direction for affective decision making beyond direct mapping or unconditional compliance. Full article

Against the dual backdrop of worsening air pollution and industrial intelligent transformation, industrial robot technology has become an important means to promote air pollution synergistic governance. This study innovatively incorporates public environmental satisfaction and industrial robot application as dynamic mechanism variables, constructing an evolutionary game model involving the government, industrial enterprises, and the public. Through theoretical analysis and numerical simulation, the study reveals the influence mechanism of key cost–benefit parameters on stakeholders’ strategic interaction and the system’s evolution path. The conclusions are as follows: (1) The government’s environmental supervision directly affects enterprises’ green transformation willingness, and enterprises’ behavior reversely impacts public satisfaction and supervision effectiveness, forming a “supervision–response–feedback” closed-loop. (2) The cost and benefit parameters related to industrial robots are crucial for the evolution of the game system, and there is significant heterogeneity in their impact on the strategic choices of the three parties. The robot adaptation transformation of enterprise industrial depends on the comprehensive consideration of the transformation cost and the green benefits. Public supervision is regulated by both the supervision cost and the incentive benefit. The government regulation takes into account both the regulatory cost and the loss of social reputation. Various parameters dynamically regulate the system’s equilibrium by altering the party’s cost–benefit structure. (3) The application of industrial robots and the feedback of public environmental satisfaction form a coupling effect, jointly determining the long-term evolution direction of the game system. When the cost benefit and supervision incentives are well-matched, enterprises will actively promote the green transformation of industrial robots in order to achieve intelligent pollution control. The effectiveness of public supervision has also been fully realized. The dynamic adaptation of the two components can lead the system towards an efficient and stable equilibrium in air pollution governance. Full article

As a typical biomimetic robotic system, quadruped robots replicate the flexible locomotion of quadruped mammals, outperforming wheeled robots in human-centered daily scenarios. To improve the social navigation adaptability of biomimetic quadruped robots in human–robot shared environments, this paper proposes a collision-aware orthogonal steering social force model (COSFM), an enhanced social force model that integrates collision prediction and social norms, inspired by human-like collision avoidance behaviors and social interaction rules. The model addresses key limitations of conventional social force models: delayed responses to dynamic pedestrians and inadequate consideration of pedestrians’ comfort zones. It introduces a time-to-collision prediction mechanism to mimic human predictive decision-making in dynamic social interactions, enhancing the robot’s anticipation of pedestrian motion intentions, and designs an orthogonal steering-based avoidance strategy for four typical human–robot interaction scenarios (head-on encounters, intersecting paths, active overtaking, passive yielding). This strategy replicates humans’ natural priority of lateral steering over abrupt deceleration or retreat, generating socially compliant trajectories aligned with human behavioral expectations. The proposed method is validated via simulation and real-world experiments on a Unitree Aliengo quadruped robot. Results show that the COSFM algorithm achieves a higher navigation success rate and better performance in path length, navigation time, and minimum human-robot distance than existing approaches, while its human-like lateral avoidance priority effectively preserves pedestrians’ psychological comfort zones, demonstrating robust social adaptability and great application potential for biomimetic legged robots. Full article

Service robots have the potential to support cognitive and social well-being in long-term care facilities, yet their widespread adoption depends on intuitive interaction modalities that minimize user learning effort and the need for a technical expert on-ground. Spoken dialogue is a natural interface, and recent advances in large language models (LLMs) promise more flexible and engaging exchanges than traditional scripted systems. In this study, we implemented a modular speech-based architecture combining automatic speech recognition, text-to-speech synthesis, and a conversational agent capable of switching between a fully scripted and LLM-driven dialogue. The implemented architecture was embodied in a TIAGo robot (PAL Robotics) and tested to compare three conversational strategies: (1) scripted, pre-defined dialogue, (2) LLM-based free-form conversation, and (3) LLM-based conversation augmented with personal information provided through the prompt. Eighteen younger adults and eighteen older adults engaged in a five-minute interaction with the robot under all three conditions in a within-subject design, and subsequently completed the Almere model questionnaire. Across all subscales and both participant groups, differences between dialogue strategies were small and statistically non-significant, despite informal comments from several older participants indicating a perceived increase in intelligence or naturalness for the LLM conditions. The findings suggest that generative dialogue and basic personalization alone do not meaningfully shift perceived acceptance in brief, task-neutral encounters, underscoring the importance of longer-term deployment and functionally meaningful robot roles in future evaluations. Full article

Autism Spectrum Disorder (ASD) is defined by ongoing difficulties in social communication, flexibility in behavior, and adaptive learning skills. Interventions that utilize robots have demonstrated potential in providing organized training for children with ASD; however, there is a lack of controlled studies that specifically examine the effects of reinforcement strategies. This research introduces a systematic interaction policy based on reinforcement, founded on the principles of Applied Behavior Analysis (ABA), and assesses its effectiveness through a randomized controlled experimental design with observation. The humanoid robot NAO was used in two different interaction scenarios, one involving a reinforcement condition (RC) and the other a non-reinforcement condition (RC), ensuring that the instructional material and environment were maintained, while only the availability of contingent positive feedback was altered. A total of 50 participants diagnosed with ASD Level 2 engaged in structured word-learning sessions. Learning outcomes were assessed using institutional performance criteria, average response time, and emotion analysis derived from a CNN-based facial expression model. Independent samples t-tests revealed statistically significant improvements in both performance scores (t(48) = 3.779, p < 0.05) and response times (t(48) = 3.758, p < 0.05) in the reinforcement condition compared to the non-reinforcement condition. The findings demonstrate that structured ABA-based reinforcement within robotic interaction significantly enhances learning efficiency and task engagement, contributing methodologically rigorous evidence to robot-assisted ASD intervention research. Full article

Generative artificial intelligence and foundation models have changed machine learning by allowing systems to produce readable text, realistic images, and other multimodal content with little direct input from a user. Foundation models are large neural networks trained on very large and varied datasets, and they form the core of many current generative AI (GenAI) systems. Their rapid development has led to major advances in areas like natural language processing, computer vision, multimodal learning, and robotics. Examples include GPT, LLaMA, and diffusion-based architectures, such as models often used for image generation. Systems such as Stable Diffusion show this shift by illustrating how AI can interpret information, draw basic inferences, and produce new outputs using more than one type of data. This review surveys common foundation model architectures and examines what they can do in generative tasks. It reviews Transformer, diffusion, and multimodal architectures, focusing on methods that support scaling and transfer across domains. The paper also reviews key approaches to pretraining and fine-tuning, including self-supervised learning, instruction tuning, and parameter-efficient adaptation, which support these systems’ ability to generalize across tasks. In addition to the technical details, this review discusses how GenAI is being used for text generation, image synthesis, robotics, and biomedical research. The study also notes continuing challenges, such as the high computing and energy demands of large models, ethical concerns about data bias and misinformation, and worries about privacy, reliability, and responsible use of AI in real settings. This review brings together ideas about model design, training methods, and social implications to point future research toward GenAI systems that are efficient, easy to interpret, and reliable, while supporting scientific progress and ethical responsibility. Full article

Background: Positive and existential psychiatry are approaches to mental health that emphasize the promotion of well-being, resilience, and optimal functioning alongside the conventional management of mental illness. Research suggests that the development of self-regulatory metacompetencies is associated with positive mental health and well-being outcomes. Artificial intelligence (AI) technologies are increasingly being used as assistive tools in psychiatry. However, the integration of AI in therapeutic interventions remains underexplored. Objectives: Thus, this systematic review aimed to synthesize evidence from randomized controlled trials evaluating whether AI-based positive and existential psychiatry interventions contribute to improvements in mental and emotional health. A second objective was to examine whether the therapeutic components and psychological processes implemented in these interventions conceptually relate to self-regulatory metacompetencies that underpin sustainable mental health and human flourishing. Methods: The review was conducted according to PRISMA 2020 guidelines. Only experimental studies including randomized controlled trials (RCTs) published from 2015 to 2025 were included. Twenty-four studies met the inclusion criteria. Results: Across interventions using conversational AI chatbots, generative AI and AI-augmented reflective systems, embodied conversational agents, social and humanoid AI robots, consistent improvements were observed in depression, anxiety, negative affect, and loneliness. The interventions enhanced various metacompetencies such as emotional regulation, emotional awareness, self-reflection, and cognitive reappraisal. Conclusions: The findings suggest that AI-based positive and existential psychiatry interventions can support mental and emotional health, especially when fostering key metacompetencies. Although promising, further high-quality trials are needed to clarify long-term effects. The findings of this study can contribute to the discussion about the ways AI-supported interventions may promote sustainable mental health. Full article

The collection of patient-reported outcome measures (PROMs) is a key measurement tool for patient-centred care. At the same time, collecting these measures poses obstacles for many patients, leading to these groups being underrepresented in the data. We have therefore developed a multimodal, AI-driven assistance system to support patients in collecting these data. The interface of the system comprised a digital tablet containing the PROM questionnaire items and the assistant in three forms of embodiment: A virtual avatar, a physical avatar, and a voice-only agent. To evaluate the users’ experience and ratings of the system, two separate studies were implemented in two rehabilitation centers with 195 patients. A mixed within–between RCT was conducted at an outpatient clinic, where patients completed PROMs both with and without an assistant, and a between-subject design at an inpatient clinic comparing routine PC-based care with avatar- and robot-assisted PROM administration. Our results suggest a preference for the non-assisted tablet-only condition in Clinic A, whereas, in Clinic B, both agent conditions were preferred over routine care. We have further analyzed aspects such as trust and social presence in this study to gain a more thorough understanding of the users’ experience. Our analysis shows a higher trust rating for the voice-only assistant, whereas the robot, virtual avatar, and the voice-only conditions were perceived as more socially present. The impact of demographic factors and affinity for technology on the user ratings was also thoroughly studied. Our findings shed light on the role of agent embodiment in PROM assistance and contribute to the future design and evaluation of effective, engaging, and trustworthy systems for data collection in healthcare settings. Full article

In the context of advancing manufacturing, production systems are shifting toward human-centric and personalized production. However, accurately quantifying subjective user needs into precise product specifications remains a challenge. Taking child companion robots as an example, this paper proposed a novel product innovation design framework based on Extenics and Kansei engineering to optimize the texture design of smart products. By systematically integrating synergistic relationships among colour, material, and surface processing technology, the framework aimed to enhance the sustainable value and social sustainability of products by more precisely meeting users’ perceptual and emotional needs. The research methodology employed the semantic differential method to quantify user perception and utilized the K-means clustering algorithm to construct a chromatic colour sample library for smart products. Subsequently, by combining the multi-criteria decision-making tool grey relational analysis with statistical verification, the optimal design scheme was selected from the generated alternatives. Experimental results demonstrated that this method significantly reduced design subjectivity and ambiguity. By bridging the gap between user expectations and engineering solutions, the framework provides a systematic solution for mass customization and process optimization that promotes resource efficient and sustainable production, while also reducing the resource waste associated with traditional trial and error design processes. Full article