Social Robots - Robotics and Toy Computing

Permanent URI for this collectionhttps://hdl.handle.net/10125/112439

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Now showing 1 - 7 of 7

Mixed Reality and Social Robots for Meditation: A Review and Recommendation
(2026-01-06) Tseng, Hao An; Badalov, Khusrav; Hung, Patrick; Yoon, Young
Mixed Reality (MR) technology seamlessly integrates virtual and physical environments, offering new possibilities for human interaction. This paper reviews the synergistic convergence of MR with social robots, agents designed for natural and engaging human interaction, to create a new paradigm for meditation. This approach addresses the challenges of traditional meditation, such as the need for a quiet environment and sustained attention. It achieves this by leveraging MR to provide personalized, immersive virtual worlds while using social robots to offer embodied guidance, haptic feedback, and a sense of social co-presence. The integration of biofeedback, including heart rate monitoring and Electroencephalogram (EEG) analysis, allows real-time personalization that can improve user engagement and reduce barriers to practice. Through an analysis of empirical evidence, this review highlights the efficacy of this combined approach in mental health interventions for conditions such as anxiety and stress. The paper also provides a comprehensive study of the applicability, user acceptance, and significant technical and ethical challenges inherent in the integration of robotic agents with immersive therapeutic technologies.
A Review of EEG and Eye-Tracking Applications in Human-Robot Interaction Across Domains
(2026-01-06) Yang , Yu-Ling; Li, Chia-Ming; Hsu, Chiao-Ning; Liu, Wan-Yi; Chen, Ai-Ai; Kuo, Meng-Chi; Chien, Shih-Yi
With advances in electroencephalography (EEG) and eye-tracking, these physiological sensing methods have become vital in human-robot interaction (HRI) research. This review systematically examines their applications across education, healthcare, industry, and social domains, covering both single-sensor uses and integrated EEG–eye-tracking studies. We analyze experimental designs, data synchronization, signal fusion, technical challenges, and potential benefits. This review highlights the importance of multimodal physiological data in improving HRI quality and effectiveness. This review provides practical guidance for leveraging emerging technologies to enhance user experiences, stimulate innovation, and shape future research in intelligent interactive systems through multimodal sensing.
Designing Socially Grounded Data Pipelines for Training and Operating Socially Intelligent Robots: Challenges and Future Directions
(2026-01-06) Elkins, Aaron; Singh, Sanchit; Pourebadi, Mary; Amadasun, Uyiosa; Chadha, Aman; Abhari, Kaveh
Designing socially intelligent robots presents a frontier challenge in human–robot interaction and information systems research, where technical architectures must align with the embodied, context-rich demands of social life. Current Vision–Language–Action (VLA) models integrate multimodal inputs but fall short in supporting socially coherent behavior, limiting their value as training pipelines. Using a clinical problematization approach, this study examines experimental pipelines to identify structural limitations in temporal coherence, continuity, multimodal integration, and affective inference. These shortcomings reveal a deeper misalignment between prevailing training paradigms and the sociotechnical requirements of interactional integrity. In response, we highlight perception–language models (PLMs) as a more socially attuned substrate, capable of extracting contextual signals and enhancing situational grounding for behavior modeling. We conclude by outlining a research agenda that advances spatiotemporal reasoning, affective modeling, and embodied coherence, thereby contributing to IS discourse on the design of trustworthy, socially adaptive robotic systems.
On the Dark Side of AI Companions – How a Parasocial Preference for a Social Chatbot Can Lead to Pathological Chatbot Use
(2026-01-06) Fröbel, Lara; Louis, Alexander; Kenning, Peter
Social chatbots are becoming increasingly popular due to their ability to simulate human interactions and build socio-emotional relationships with users. However, along with the positive effects come potential risks. For example, a parasocial preference for a social chatbot could contribute to excessive and unregulated chatbot use, jeopardizing users' well-being. As the potential harms associated with social chatbots are largely unexplored, there is a need to integrate theories into the IS literature that explain the negative effects of a parasocial preference for a social chatbot. Therefore, we apply the cognitive- behavioral model of pathological internet use to human-chatbot interactions. We conducted two empirical studies and found that a parasocial preference contributes to deficient self-regulation regarding chatbot use and promotes the use of the chatbot for mood regulation. Furthermore, deficient self-regulation increases the risk of pathological chatbot use, especially among lonely users.
Designing Conversational AI for Social Robots in Corporate Contexts: A Case Study on Customizing LLMs through Action Research
(2026-01-06) Leichtle, Marcel; Schultze, Sven; Schönfeld, Nils Lucas; Homburg, Nadine; Stock-Homburg, Ruth Maria
This paper presents a case study on customizing Large Language Models (LLMs) for social robots in corporate environments. Over a year-long collaboration with an enterprise, we explored how LLMs can be integrated into multimodal assistants that operate across both embodied robot platforms and flexible digital interfaces. Using three iterative action research cycles, we developed and evaluated a scalable customization framework. We introduce Knowledge Interaction Distillation (KID), a method for generating synthetic training data from simulated user interactions, enabling efficient finetuning of task-specific models. Through participatory design, controlled experiments, and field deployment, we identified core requirements and validated a distributed system architecture that supports multimodal interaction. Our findings offer actionable insights for deploying conversational AI for social robots not as isolated systems, but as part of a broader ecosystem aligned with real-world workflows and organizational constraints.
Understanding Physicians’ Continued Use of Robotic Surgical Navigation Systems: An Integrated TAM–TPB Perspective
(2026-01-06) Lo, Chia-Lun; Tseng, Hsiao-Ting
Robotic surgical navigation systems have become increasingly important in modern surgery, offering precision and consistency that enhance clinical outcomes. Despite their growing adoption, limited attention has been paid to the perspectives of physicians who directly operate these systems in clinical practice. To address this gap, this study investigates the determinants of physicians’ continued use of robotic surgical navigation systems by integrating the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB). Data were collected from 120 orthopedic surgeons across seven major medical centers in Taiwan, with 115 valid responses (96% response rate). Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results reveal that task complexity and social influence significantly affect perceived usefulness, while system self-efficacy and facilitating conditions influence perceived ease of use. Perceived ease of use indirectly impacts continuance intention through perceived usefulness, and top management support is identified as a critical reinforcing factor. These findings extend TAM and TPB to advanced surgical technologies and offer practical implications for promoting long-term adoption in clinical settings.
Introduction to the Minitrack on Social Robots - Robotics and Toy Computing
(2026-01-06) Marques Peres, Sarajane; Hung, Patrick; Huang, Shih-Chia

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