From world models to active agents: the next step for physical AI

Generative AI is advancing from producing realistic images and videos toward physical AI systems that can sense, reason, plan, and act in changing real-world environments, a focus of the CVPR 2026 Workshop on World Models Meet Active Sensing and Closed-Loop Planning. Workshop research emphasized that visual plausibility alone does not make a world model useful for robotics: RoboWM-Bench found that generated manipulation videos can fail in execution because of errors in geometry, contact, and physics, while GEM-4D improved reported real-world manipulation success by enforcing geometric consistency and converting predictions into robot trajectories. SAW-Bench also identified substantial shortcomings in multimodal models’ situated spatial awareness, showing that agents must understand scenes relative to their own viewpoints and available actions. The workshop framed active sensing as a key capability, enabling agents to seek additional visual or tactile information when uncertainty prevents reliable action. Together, world models, active sensing, and closed-loop planning form a continuous perception-action cycle in which agents observe, predict outcomes, choose actions, assess results, and adapt, while open research questions remain around uncertainty, representation, evaluation, and generalization to unfamiliar environments.

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