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Zero-shot object navigation is a challenging task for home-assistance robots.This task emphasizes visual grounding, commonsense inference and locomotionabilities, where the first two are inherent in foundation models. But for thelocomotion part, most works still depend on map-based planning approaches. Thegap between RGB space and map space makes it difficult to directly transfer theknowledge from foundation models to navigation tasks. In this work, we proposea Pixel-guided Navigation skill (PixNav), which bridges the gap between thefoundation models and the embodied navigation task. It is straightforward forrecent foundation models to indicate an object by pixels, and with pixels asthe goal specification, our method becomes a versatile navigation policytowards all different kinds of objects. Besides, our PixNav is a pure RGB-basedpolicy that can reduce the cost of home-assistance robots. Experimentsdemonstrate the robustness of the PixNav which achieves 80+% success rate inthe local path-planning task. To perform long-horizon object navigation, wedesign an LLM-based planner to utilize the commonsense knowledge betweenobjects and rooms to select the best waypoint. Evaluations across bothphotorealistic indoor simulators and real-world environments validate theeffectiveness of our proposed navigation strategy. Code and video demos areavailable at https://github.com/wzcai99/Pixel-Navigator.

Bridging Zero-shot Object Navigation and Foundation Models through Pixel-Guided Navigation Skill