What Matters in RL-Based Methods for Object-Goal Navigation: An Empirical Study and A Unified Framework

Jul 16, 2026

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Object-Goal Navigation (ObjectNav) enables mobile robots to find an instance of a target object category in previously unseen environments using only onboard perception. Reinforcement learning has become a leading approach, but modular systems introduce many design choices across perception, policy, and inference-time strategies whose relative effects remain unclear. The study decomposes the pipeline into perception, policy, and test-time enhancement, running controlled experiments to quantify each contribution. Results show perception quality and test-time enhancements drive larger gains than policy changes alone, and a unified framework yields state-of-the-art performance on Gibson.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages35

Reading Time1 h 28 min

Introduction

Modular Decomposition of RL-Based ObjectNav

Empirical Study of Module Contributions

Unified Framework and System Design

Benchmark Results and Human Baseline

Practical Recommendations

arXiv :2510 .01830v2 [ cs .RO] 12 Jul 2026 What Matters in RL-Based Methods for Object-Goal Navigation? An Empirical Study and A Unified Framework Hongze Wang 1, Boyang Sun 1, Jiaxu Xing2, Fan Yang3, Marco Hutter3, Dhruv Shah4, Davide Scaramuzza2, and Marc Pollefeys 1 ,5 1 Computer Vision and Geometry Group, ETH Zurich, Switzerland 2 Robotics and Perception Group, University of Zurich, Switzerland 3 Robotic Systems Lab, ETH Zurich, Switzerland 4 Princeton Robotic Intelligence and SysteMs group, Princeton University, USA 5 Microsoft, Switzerland Abstract. Object-Goal Navigation (ObjectNav) is a key capability for deploying mobile robots in everyday environments such as homes, schools, and workplaces. In this task, an agent must locate an instance of a target object category in previously unseen environments using only onboard perception, requiring the integration of semantic understanding, spatial reasoning, and long-horizon planning. Reinforcement learning (RL) has become a dominant paradigm for ObjectNav, yet modern systems involve numerous design choices across perception modules, policy architectures, and inference-time strategies. The relative impact of these components, however, remains poorly understood. In this work, we present a largescale empirical study of modular RL-based ObjectNav systems. We decompose the navigation pipeline into three key components: perception, policy, and test-time enhancement, and conduct extensive controlled experiments to analyze their individual contributions. Our results suggest that improvements in perception quality and test-time strategies often yield larger performance gains than policy improvements alone, highlighting the importance of understanding how different components interact within modular navigation systems. Motivated by these findings, we introduce a unified framework for systematically studying modular ObjectNav systems. Guided by our analysis, we build an enhanced system that achieves state-of-the-art performance on the Gibson benchmark, improving SPL by 6 .6% and success rate by 2 .7% over prior methods. We also introduce a human expert baseline, achieving 98% success, highlighting the significant gap between current RL agents and human-level navigation. Finally, we provide practical insights and design recommendations for each module to help guide future research. Project page: [https://honwang0054.github.io/What-matters-in-RL-ObjNav-web/](https://honwang0054.github.io/What-matters-in-RL-ObjNav-web/) . 1 Introduction Recent advances in computer vision and deep learning have inspired growing interest in interdisciplinary applications that bridge perception, reasoning, and 2 H. Wang et al. control, especially in robotics. Among these, vision-based navigation has emerged as a foundational capability for autonomous mobile agents. A key benchmark in this domain is Object-Goal Navigation (ObjectNav), where a robot must navigate to an instance of a specified object category in an unseen environment, relying solely on its onboard sensors [2, 5 , 24 , 35] . This task is both practically important and technically challenging: it requires semantic understanding, spatial reasoning, and long-horizon planning. Among many approaches, Reinforcement Learning (RL) has become a dominant paradigm for ObjectNav, offering a structured framework to learn directly through trial-and-error and showing steady progress across various benchmarks [4, 5 , 27 , 41] . While end-to-end RL policies are com- Fig. 1: Our framework encompasses: (1) an empirical study analyzing the impact of different modules, and (2) a unified framework with interchangeable components, enabling users to customize their own object-goal navigation policies. mon, modular RL approaches have shown greater robustness and improved generalization. By decomposing the system into interpretable and tunable components, such as perception, mapping, policy, and action execution, these methods align with the m

FAQs

What problem does Object-Goal Navigation (ObjectNav) address?

ObjectNav tasks an agent with locating an instance of a specified object category in unseen environments using only onboard sensing, requiring semantic understanding, spatial reasoning, and long-horizon planning.

How does the paper structure modular RL-based ObjectNav systems for analysis?

It decomposes the navigation pipeline into three components: perception, policy, and test-time enhancement, then designs controlled experiments to isolate each component’s contribution.

Which components most strongly affect performance in the study?

Improvements in perception quality and test-time strategies generally yield larger gains than policy improvements alone, emphasizing the interaction effects inside modular navigation systems.

Jul 16, 2026

Upload by Patrick