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RangeNav is presented, a compact (183k parameters) LiDAR range-image encoder pretrained entirely on synthetic data that demonstrates robust navigation through dense crowds, low-light settings, and construction zones where limited-field-of-view depth policies struggle.
For robots to operate reliably in real-world environments, they need to perceive their surroundings, act, and reason about the consequences of those actions. Rapid progress in the domains of representation learning, VLA models, and world models has significantly enhanced the capabilities of robot learning systems, enab...
LightNav-0 is presented, a compact generalist embodied navigation model that elicits the spatial intelligence of a pretrained VLM and aligns it with navigation, without task-specific prediction heads, and establishes compact VLMs as a unified and transferable backbone for generalist embodied navigation.
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