RANGER: A Monocular Zero-Shot Semantic Navigation Framework through Visual Contextual Adaptation

TL;DR

RANGER is a monocular, zero-shot semantic navigation framework that uses visual context to improve target localization without relying on depth or pose data.

Contribution

It introduces a novel approach combining 3D reconstruction, vision-language models, and visual in-context learning to enhance navigation in complex environments.

Findings

Achieves competitive success rates on HM3D benchmark.

Demonstrates superior visual in-context learning adaptability.

Operates effectively without prior 3D environment mapping.

Abstract

Efficient target localization and autonomous navigation in complex environments are fundamental to real-world embodied applications. While recent advances in multimodal foundation models have enabled zero-shot object goal navigation, allowing robots to search for arbitrary objects without fine-tuning, existing methods face two key limitations: (1) heavy reliance on ground-truth depth and pose information, which restricts applicability in real-world scenarios; and (2) lack of visual in-context learning (VICL) capability to extract geometric and semantic priors from environmental context, as in a short traversal video. To address these challenges, we propose RANGER, a novel zero-shot, open-vocabulary semantic navigation framework that operates using only a monocular camera. Leveraging powerful 3D foundation models, RANGER eliminates the dependency on depth and pose while exhibiting strong VICL capability. By simply observing a short video of the target environment, the system can also significantly improve task efficiency without requiring architectural modifications or task-specific retraining. The framework integrates several key components: keyframe-based 3D reconstruction, semantic point cloud generation, vision-language model (VLM)-driven exploration value estimation, high-level adaptive waypoint selection, and low-level action execution. Experiments on the HM3D benchmark and real-world environments demonstrate that RANGER achieves competitive performance in terms of navigation success rate and exploration efficiency, while showing superior VICL adaptability, with no previous 3D mapping of the environment required.