R2F: Repurposing Ray Frontiers for LLM-free Object Navigation

• URL: http://arxiv.org/abs/2603.08475v1
• Date: Mon, 09 Mar 2026 15:10:10 GMT
• Title: R2F: Repurposing Ray Frontiers for LLM-free Object Navigation
• Abstract summary: Vision-Language Models (VLMs) and Large Language Models (LLMs) are now widely used as high-level decision-makers instead of end-to-end policies.<n>We develop an LLM-free framework for indoor open-vocabulary object navigation.<n>Experiments in Habitat-sim and on a real robotic platform demonstrate competitive state-of-the-art zero-shot performance with real-time execution.
• Score: 1.4755786263360526
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Zero-shot open-vocabulary object navigation has progressed rapidly with the emergence of large Vision-Language Models (VLMs) and Large Language Models (LLMs), now widely used as high-level decision-makers instead of end-to-end policies. Although effective, such systems often rely on iterative large-model queries at inference time, introducing latency and computational overhead that limit real-time deployment. To address this problem, we repurpose ray frontiers (R2F), a recently proposed frontier-based exploration paradigm, to develop an LLM-free framework for indoor open-vocabulary object navigation. While ray frontiers were originally used to bias exploration using semantic cues carried along rays, we reinterpret frontier regions as explicit, direction-conditioned semantic hypotheses that serve as navigation goals. Language-aligned features accumulated along out-of-range rays are stored sparsely at frontiers, where each region maintains multiple directional embeddings encoding plausible unseen content. In this way, navigation then reduces to embedding-based frontier scoring and goal tracking within a classical mapping and planning pipeline, eliminating iterative large-model reasoning. We further introduce R2F-VLN, a lightweight extension for free-form language instructions using syntactic parsing and relational verification without additional VLM or LLM components. Experiments in Habitat-sim and on a real robotic platform demonstrate competitive state-of-the-art zero-shot performance with real-time execution, achieving up to 6 times faster runtime than VLM-based alternatives.

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