arXiv:2512.21201v3 Announce Type: replace-cross Abstract: Zero-shot object navigation (ZSON) requires robots to find target objects in unseen environments without task-specific fine-tuning or pre-built maps, a key capability for general-purpose service robots. Yet methods that perform well in simulation often degrade in cluttered real-world scenes with severe occlusion and latent hazards, where large unseen regions make single-scene inference brittle and unsafe. We propose Schr\"odinger's Navigator, a belief-aware framework that reasons at inference time over multiple trajectory-conditioned im
Advances in AI, particularly in generative models and probabilistic reasoning, are enabling new approaches to complex problems like zero-shot object navigation in cluttered real-world environments.
This development is critical for deploying general-purpose service robots in unstructured human environments, moving beyond simulation-bound successes to practical, safe, and robust real-world applications.
The ability to reason over multiple future trajectories under uncertainty and severe occlusion fundamentally changes the approach to robot navigation, making it more resilient and less 'brittle' in unpredictable scenes.
- · Robotics companies
- · Logistics and e-commerce
- · Elder care providers
- · AI research labs
- · Companies reliant on highly structured environments for automation
- · Traditional robotics lacking advanced AI-driven perception
General-purpose service robots become significantly more viable and reliable for deployment outside controlled industrial settings.
Increased adoption of autonomous robots in sectors like domestic assistance, retail, and last-mile delivery, leading to new service economies.
Societal shifts in labor markets and urban infrastructure as ubiquitous autonomous agents transform daily tasks and physical spaces.
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Read at arXiv cs.AI