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UT Austin
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Iterative reasoning with Vision-Language Models can drastically improve mapless navigation success rates by dynamically identifying and refining relevant environmental cues.
Forget hand-crafted rewards: VLLR leverages LLMs and VLMs to automatically generate dense rewards, boosting robotic task success rates by up to 56% on long-horizon tasks.
