Informing robot design through early public engagement: lay perceptions of soft versus rigid socially assistive and rescue robots

TL;DR

This study explores how the public perceives soft versus rigid robots in assistive and rescue roles, showing that soft robots are seen as safer and more emotionally comforting but also more fragile.

Contribution

The study introduces a scalable method for early public engagement using Cognitive-Affective Maps to inform human-centered robot design.

Findings

Post-intervention evaluations of soft robots were more positive than pre-intervention evaluations of rigid robots.

Soft robots were associated with safety and emotional comfort, while rigid robots were linked to precision and efficiency.

Public concerns about soft robots included fragility and emotional dependence, while rigid robots raised issues like data security and emotional detachment.

Abstract

As soft robots become more prevalent in society, it becomes increasingly important to understand how laypersons evaluate their risks and benefits relative to conventional rigid robots. This article investigates public perceptions of soft versus rigid embodiments of socially assistive robots (SAR) and rescue robots (RR) and explores how these perceptions can inform early-stage robot design. We conducted an online study, using a scenario-based intervention design combined with Cognitive-Affective Maps (CAMs) to capture participants’ cognitive–emotional belief structures. In a first step, participants constructed CAMs depicting perceived risks and benefits of rigid SAR or RR. After reading a second scenario introducing the corresponding soft robot, they revised their maps, allowing a direct contrastive comparison between the first (rigid) and second (soft) scenario. Quantitative analyses showed that, across both application domains, post-intervention evaluations (after the soft-robot scenarios) were more positive than pre-intervention evaluations of rigid robots. Qualitative analyses revealed distinct argument structures: After learning about soft robots, participants added concepts emphasizing safety, emotional comfort, and adaptability, but also introduced concerns such as fragility and emotional dependence, whereas rigid robots were linked to precision, robustness, and efficiency, alongside worries about technical failure, data security, and emotional detachment. By integrating intervention-based CAMs with data-driven qualitative synthesis, the study demonstrates a scalable method for early public engagement that uncovers how laypersons qualitatively negotiate trade-offs between soft and rigid designs in plausible early-stage scenarios. These insights provide actionable input for human-centered design of soft robots, supporting responsible and socially aligned robot development.

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