World Models in Physical AI

We propose World Models for Physical AI, a one-day NeurIPS 2026 workshop on learned models of physical-world dynamics and their use as the computational substrate for embodied, real-world AI systems. World models have rapidly moved from a research curiosity to a central organizing idea for robotics, autonomous driving, and embodied agents: action-conditioned video generators, latent dynamics models, and generative simulators are now used to train policies, evaluate agents, and even act directly in the physical world. Yet the communities of generative modeling, reinforcement learning, robotics, and computer vision, which are driving this progress, remain fragmented across venues. This workshop brings them together to crystallize the shared problems of evaluation, controllability, long-horizon consistency, sim-to-real, and safety, to chart a research agenda for world models that are actionable, physically grounded, and deployable.