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

TL;DR

This study investigates how human characteristics and interaction types affect perceptions of collaborative robots, emphasizing the importance of inclusive, prosocial design and the potential of reflective methods to gather nuanced user feedback.

Contribution

It introduces a novel online assessment approach combining video evaluations with reflective exercises to understand diverse user perceptions of robot behavior.

Findings

Antisocial robot behavior rated lowest across scenarios.

Interactions with aged individuals elicited more sensitive evaluations.

Object handovers were viewed more positively than other collaboration types.

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.