R2F: Repurposing Ray Frontiers for LLM-free Object Navigation
Authors: Francesco Argenziano, John Mark Alexis Marcelo, Michele Brienza, Abdel Hakim Drid, Emanuele Musumeci, Daniele Nardi, Domenico D. Bloisi, Vincenzo Suriani
Organizations: Department of Computer, Automation and Management Engineering, Sapienza University of Rome, Rome, Italy · Department of Electronics and Automation, Mohamed Khider University of Biskra, Biskra, Algeria · International University of Rome UNINT, Rome, Italy
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.
Figures & tables
Fig. 2: R2F system overview and execution schedule. The agent receives RGB-D observations and a text query. RGB images are processed by RADIO with a Neighborhood-Aware attention modification (NA) to produce dense open-vocabulary features (NA-RADIO), while the query is encoded with SigLIP. From out-of-range depth pixels, semantic rays are sampled, binned by direction, and associated with frontier regions, forming Semantic Ray Frontiers that accumulate directional semantic evidence. In parallel, depth updates a volumetric occupancy map, from which frontier regions are periodically recomputed ( low frequency ), while semantic ray accumulation runs continuously ( high frequency ). Frontier regions are scored via cosine similarity with the query embedding, and the navigation policy ( R2F or R2F-VLN ) selects and tracks the highest-scoring frontier until the goal is detected or exploration continues.
Fig. 3: NA-RADIO’s feature maps in comparison with different text query. Both the text query embedding and the visual features lie in the SigLIP [ 37 ] embedding space thanks to RADIO’s adapter. In practice, we observe that informative similarity values typically lie in the range [−0.10,0.15] .
Task
Method
SR (%)
SPL (%)
t (s)
ObjectNav
VLN-Game [ 2 ]
76.7
28.0
122.0
3D-MEM [ 31 ]
26.7
15.9
61.2
VLFM [ 5 ]
40.0
7.71
84.2
OpenFrontier [ 8 ]
23.3
7.12
245.0
R2F (ours)
78.3
29.6
32.7
VLN
VLN-Game [ 2 ]
43.7
22.7
504.0
TABLE I: Comparison across ObjectNav and VLN tasks with the three baselines. We report SR , SPL , and Execution Time ( t ). Bold is best and underlined is second-best.
Fig. 4: Pipeline execution on a real robot for the “Find a sink” goal. The robot navigates between the boundaries based on their semantic value until it reaches the goal.
School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China. · Joint Research Laboratory for Embodied Intelligence, Xinjiang University. · Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing, Xinjiang University.
The authors are with the Shanghai Key Laboratory of Navigation and Location Based Services, Shanghai Jiao Tong University (SJTU), Shanghai 200240, China. · Litao Wei is also with Zhiyuan College, SJTU. · Ling Pei is also with the State Key Laboratory of Submarine Geoscience, SJTU.