Sebastian Sartor, Robert Seamans, Neil Thompson · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.24267 · cited by 0 works (OpenAlex) · open-access PDF
Les indices d'exposition uniques qui cartographient les capacités techniques sur les compétences professionnelles sont devenus un outil standard pour anticiper comment l'intelligence artificielle peut remodeler le travail, mais les mesures existantes couvrent soit l'intelligence artificielle logicielle ou la robotique proxy par des brevets et des données d'installation qui reflètent les robots industriels des dernières décennies. Nous construisons les premières mesures d'exposition basées sur les compétences pour la robotique et l'intelligence artificielle physique, et le premier pour évaluer la relation de chaque capacité de contenu avec les capacités de travail 52 ONET, et les géographies. Après le cadre de Felten et al. (ROE) (2018, 2021), nous construisons une taxonomie des capacités
Machine translation (Meta M2M100); the English original follows.
Exposure indices that map technical capabilities onto occupational abilities have become a standard tool for anticipating how AI may reshape work, but existing measures either cover software AI or proxy robotics through patents and installation data that reflect the industrial robots of past decades. We construct the first abilities-based exposure measures for robotics and physical AI, and the first to span occupations, industries, and geographies. Following the framework of Felten et al. (2018, 2021), we build a robotics capability taxonomy of 16 capabilities in eight domains from 78 academic, industry, and standards sources, use a large language model to score the relatedness of each capability to the 52 ONET work abilities, and aggregate to Robotics Occupational Exposure (ROE) for 774 occupations, Robotics Industry Exposure (RIE) for 250 NAICS industries, and county-level Robotics Geographic Exposure (RGE). We find that robotics exposure concentrates in physically intensive work such as construction, farming, transportation, and production, and in industries such as logging, mining, trucking, and warehousing. Unlike generative-AI exposure, ROE is higher in occupations with lower wages, lower education requirements, and less creative-ability content. Physical collaboration and assistance, terrain-based locomotion, grasping and handling, and task and functional reasoning move the exposure ranking most, yet no single capability carries the index. Because the measures capture technical relatedness rather than readiness, cost, or adoption, they should be read as an upper bound on where physical automation could affect work.
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Topics: Robotics, Artificial intelligence, Automation, Robot, Computer science, Ranking (information retrieval)
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