Year: 2026
Type: article
Abstract: Physical AI aims to enable systems to perceive, reason, and act in the unstructured physical world, yet it faces bottlenecks of scarce real-world data and high deployment risks. Traditional hand-engineered simulators provide controllable environments yet struggle with scalable coverage of diverse scenes and long-tail scenarios. Cosmos addresses these challenges as a data-driven world foundation model platform, unifying generation- verification loop to form a closed-loop data flywheel for Physical AI development. This tutorial systematically introduces Cosmos's multi-modal design, core components, and practical deployment, offering a comprehensive guide for leveraging learned world models in physical system training and evaluation.
DOI: 10.1145/3862336
Authors: Yang Liu, Haiwei Dong, Abdulmotaleb El Saddik
Institution: University of Ottawa
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7225418128