arXiv:2510.11014v2 Announce Type: replace-cross Abstract: Autonomous robots often view rooms only partially, through a doorway, where the walls and scene structure hide the geometry and task-relevant semantics needed for safe navigation and goal-directed action. We ask whether off-the-shelf pretrained generative vision models can derive this missing structure as zero-shot offline priors for robot reasoning. Such priors should support spatio-semantic queries over unobserved structure, estimating the target object likelihood in hidden regions and the probability that those regions are occupied.
Advances in generative AI models are rapidly enabling new applications, making this research a timely exploration of their practical utility in robotics for understanding unobserved environments.
This development allows robots to infer hidden environmental information, significantly enhancing their autonomy, safety, and capability for goal-directed actions in complex, partially observed spaces.
Robots can now leverage generative AI to predict unseen spatial geometry and semantics, moving from reactive navigation to proactive planning based on inferred environmental states.
- · Robotics industry
- · Generative AI developers
- · Logistics and warehousing sector
- · Traditional sensor-reliant robotics
Improved robotic efficiency and safety in cluttered and dynamic environments.
Accelerated deployment of autonomous systems in diverse fields such as elder care, domestic help, and hazardous material handling.
Enhanced human-robot collaboration as robots gain more contextual understanding of their surroundings without explicit prior mapping.
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Read at arXiv cs.AI