🤖 AI Summary
The robotics field has long invoked “dull, dirty, and dangerous” (DDD) tasks as a primary motivation for technological deployment, yet it has largely neglected to define DDD explicitly or provide concrete task examples, thereby hindering deeper understanding of human–robot labor dynamics. This study systematically examines robotics literature from 1980 to 2024 that references DDD, integrating empirical insights from social science on DDD work through bibliometric analysis and interdisciplinary review. We construct a labor-context-oriented framework for evaluating robotic technologies and find that only 2.7% of surveyed papers define DDD, and merely 8.7% offer specific task instances. The proposed framework offers the robotics community a novel perspective for assessing the socio-technical implications of automation, fostering more responsible and socially grounded technological development.
📝 Abstract
In robotics, the concept of"dull, dirty, and dangerous"(DDD) work has been used to motivate where robots might be useful. In this paper, we conduct an empirical analysis of robotics publications between 1980 and 2024 that mention DDD, and find that only 2.7% of publications define DDD and 8.7% of publications provide concrete examples of tasks or jobs that are DDD. We then review the social science literature on"dull,""dirty,"and"dangerous"work to provide definitions and guidance on how to conceptualize DDD for robotics. Finally, we propose a framework that helps the robotics community consider the job context for our technology, encouraging a more informed perspective on how robotics may impact human labor.