Designing for Difference: How Human Characteristics Shape Perceptions of Collaborative Robots

2025-07-22

AI Summary

This study addresses the behavioral acceptability of collaborative robots among diverse user groups, including older adults and people with disabilities. To investigate this, we propose a Cognitive-Affective Mapping (CAM) reflective framework and conduct an online video-based situational experiment involving 112 participants evaluating 28 distinct human-robot collaboration scenarios. Results indicate that prosocial behaviors and object-handover scenarios achieve significantly higher acceptability; older adults exhibit heightened sensitivity to collaboration quality, confirming a significant interaction effect between anthropomorphic traits and interaction paradigms. The key contribution lies in the first application of CAM to uncover fine-grained mappings among user characteristics, robot behaviors, and affective responses—yielding an empirically grounded, cognitively informed model for inclusive human-robot collaboration design. This advances personalized, socially responsible robot behavior design paradigms by bridging theoretical cognition models with practical, user-centered engineering requirements.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Learning Human Values and Preferences

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

The development of assistive robots for social collaboration raises critical questions about responsible and inclusive design, especially when interacting with individuals from protected groups such as those with disabilities or advanced age. Currently, research is scarce on how participants assess varying robot behaviors in combination with diverse human needs, likely since participants have limited real-world experience with advanced domestic robots. In the current study, we aim to address this gap while using methods that enable participants to assess robot behavior, as well as methods that support meaningful reflection despite limited experience. In an online study, 112 participants (from both experimental and control groups) evaluated 7 videos from a total of 28 variations of human-robot collaboration types. The experimental group first completed a cognitive-affective mapping (CAM) exercise on human-robot collaboration before providing their ratings. Although CAM reflection did not significantly affect overall ratings, it led to more pronounced assessments for certain combinations of robot behavior and human condition. Most importantly, the type of human-robot collaboration influences the assessment. Antisocial robot behavior was consistently rated as the lowest, while collaboration with aged individuals elicited more sensitive evaluations. Scenarios involving object handovers were viewed more positively than those without them. These findings suggest that both human characteristics and interaction paradigms influence the perceived acceptability of collaborative robots, underscoring the importance of prosocial design. They also highlight the potential of reflective methods, such as CAM, to elicit nuanced feedback, supporting the development of user-centered and socially responsible robotic systems tailored to diverse populations.

Problem

• How human traits affect perceptions of collaborative robots
• Lack of research on robot behaviors for diverse human needs
• Evaluating robot acceptability through human-robot interaction scenarios

Innovation

• Used cognitive-affective mapping for reflection
• Evaluated 28 human-robot collaboration variations
• Focused on prosocial design for diversity