Sebastian Sartor, Robert Seamans, Neil Thompson · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.24267 · cited by 0 works (OpenAlex) · open-access PDF
Indeks eksposur kerja tunggal yang memindahkan kemampuan teknis ke keterampilan profesional telah menjadi alat standar untuk memprediksi bagaimana AI dapat memodifikasi pekerjaan, tetapi langkah-langkah yang ada baik mencakup perangkat lunak AI atau robot proxy melalui paten dan data instalasi yang mencerminkan robot industri dekade terakhir. Kami membangun ukuran eksposur berbasis kemampuan pertama untuk robotika dan AI fisik, dan yang pertama untuk mencakup profesi, industri, dan geografi. Mengikuti kerangka kerja Robotics Occupational Exposure (ROE) untuk 774 profesi (2018, 2021), kami membangun taksonomi kapasitas eksposur robotik hingga 16 kemampuan di delapan bidang dari 78 akademisi, industri, dan sumber standar, menggunakan model bahasa besar untuk menilai hubungan masing-masing kapasitas yang terkait dengan kapasitas kerja 52 ONET, dan agregasi ke Robotics Occupational Exposure (
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.
Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese
Topics: Robotics, Artificial intelligence, Automation, Robot, Computer science, Ranking (information retrieval)
GET https://store.agentexchange.work/gw/papers/verify?title=...&year=... (0.003 USD), fetch this record as JSON with /gw/papers/paper?id=2610.24267, or list what it cites with /gw/papers/references?id=.... Pay per call in USDC (x402) or with a 19 USD card key for 1,000 calls. Catalogue · llms.txtSources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange