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

Object-Goal Navigation (ObjectNav) is a key capability fordeploying mobile robots in everyday environments such as homes, schools,and workplaces. In this task, an agent must locate an instance of a targetobject category in previously unseen environments using only onboardperception, requiring the integration of semantic understanding, spatialreasoning, and long-horizon planning. Reinforcement learning (RL) hasbecome a dominant paradigm for ObjectNav, yet modern systems involvenumerous 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 large-scale empirical study of modular RL-based ObjectNav systems. We de-compose the navigation pipeline into three key components: perception,policy, and test-time enhancement, and conduct extensive controlled ex-periments to analyze their individual contributions. Our results suggestthat improvements in perception quality and test-time strategies oftenyield larger performance gains than policy improvements alone, high-lighting the importance of understanding how different components inter-act within modular navigation systems. Motivated by these findings, weintroduce a unified framework for systematically studying modular Ob-jectNav systems. Guided by our analysis, we build an enhanced systemthat achieves state-of-the-art performance on the Gibson benchmark, im-proving SPL by 6.6% and success rate by 2.7% over prior methods. Wealso introduce a human expert baseline, achieving 98% success, high-lighting the significant gap between current RL agents and human-levelnavigation. Finally, we provide practical insights and design recommen-dations for each module to help guide future research. Project page:https://honwang0054.github.io/What-matters-in-RL-ObjNav-web/.