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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.
Synthesizing plausible viewpoint and pose variations near expert trajectories, combined with post-hoc uncertainty recalibration, yields surprisingly robust waypoint prediction for robots navigating challenging environments like staircases.
