The RoboAtlas: Mapping the Global Robotics Landscape
Jiacheng Zhang, Shuo Sun, Vicky Charisi, Xinru Wang, Chen Xinyue, Zhexuan Ma, Alok Prakash, Thomas Malone
Abstract
Structured, model-level information on the world’s robot systems remains scarce: existing reports often provide aggregated market statistics, while industry directories typically stop at company-level information. In this work, we present an LLM-assisted, web-grounded analysis pipeline for studying the global robotics landscape at the robot-model level. The method combines company discovery, iterative verification, and model-level extraction of robot type, target industries, release year, and task descriptions from open-web evidence. Applying this pipeline, we study 8,229 robot models associated with 1,062 companies across 50 countries and 6 continents. Our findings reveal strong geographic concentration in the United States, China, and Japan, rapid growth after 2017, and substantial diffusion of robotics beyond manufacturing into logistics, healthcare, education, and household settings. Our work illustrates both the promise and certain limitations of LLM-assisted web analysis for large-scale robotics landscape mapping.