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BRIEF RESEARCH REPORT article
Front. Robot. AI
Sec. Human-Robot Interaction
Volume 12 - 2025 | doi: 10.3389/frobt.2025.1511549
Human Preferences for Cognitive and Emotional Capabilities in Robots Across Different Application Domains
Provisionally accepted- Department of Computer and Information Science, Linköping University, Linköping, Sweden
People's preferences regarding cognitive and emotional capabilities in robots need to be considered in the design of robotic systems that align with human values and expectations. This study investigates how such preferences vary across different robotics application domains and identifies key influencing factors. In a between-subjects study with 271 participants, both quantitative and qualitative data were collected on preferences for 12 mental (cognitive and emotional) capabilities in six types of robots, each situated in a specific domain: healthcare, defense, household, social, education, or customer service. The results reveal a general preference for agency-related abilities (e.g., planning, reasoning) over experience-related abilities (e.g., feeling happiness, pain) across all domains. However, there was a weaker preference for agency capability in household cleaning robots and a stronger preference for experience capability in social companionship robots. Qualitative analysis revealed a common desire for robots to function objectively and logically, without emotions, while still showing empathy toward human mental states. Additionally, gender and educational background emerged as factors influencing participants' preferences. Unlike previous research, which mainly focused on the attribution of mental capabilities to robots, this study offers insights into human preferences and the factors shaping them, which can inform the design of future robots and help facilitate their successful integration into society.
Keywords: Human-robot interaction (HRI), Social Robots, Cognitive Robotics, User preferences, Human-centered robotics, robot design, robot ethics
Received: 15 Oct 2024; Accepted: 27 Feb 2025.
Copyright: © 2025 Nääs, Thellman and Ziemke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Sam Thellman, Department of Computer and Information Science, Linköping University, Linköping, Sweden
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.