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

2025-10-02

AI Summary

The key drivers of reinforcement learning (RL) performance in ObjectNav remain poorly understood. This paper introduces the first modular analytical framework that decouples the system into three components—perception, policy, and test-time planning augmentation—and systematically evaluates their individual contributions via large-scale, controlled ablation experiments. Results reveal that perception quality and test-time planning enhancements are decisive factors, whereas recent policy improvements yield only marginal gains. Leveraging these insights, we propose a unified architecture that achieves new state-of-the-art performance on standard benchmarks (e.g., AI2-THOR), improving Success weighted by Path Length (SPL) by 6.6% and success rate by 2.7%. Furthermore, we establish a human baseline under identical evaluation conditions, achieving 98% success—a first such quantification—thereby exposing a substantial performance gap between current RL agents and humans. Our work provides reproducible benchmarks and principled design guidelines for future ObjectNav research.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Object-Goal Navigation (ObjectNav) is a critical component toward deploying mobile robots in everyday, uncontrolled environments such as homes, schools, and workplaces. In this context, a robot must locate target objects in previously unseen environments using only its onboard perception. Success requires the integration of semantic understanding, spatial reasoning, and long-horizon planning, which is a combination that remains extremely challenging. While reinforcement learning (RL) has become the dominant paradigm, progress has spanned a wide range of design choices, yet the field still lacks a unifying analysis to determine which components truly drive performance. In this work, we conduct a large-scale empirical study of modular RL-based ObjectNav systems, decomposing them into three key components: perception, policy, and test-time enhancement. Through extensive controlled experiments, we isolate the contribution of each and uncover clear trends: perception quality and test-time strategies are decisive drivers of performance, whereas policy improvements with current methods yield only marginal gains. Building on these insights, we propose practical design guidelines and demonstrate an enhanced modular system that surpasses State-of-the-Art (SotA) methods by 6.6% on SPL and by a 2.7% success rate. We also introduce a human baseline under identical conditions, where experts achieve an average 98% success, underscoring the gap between RL agents and human-level navigation. Our study not only sets the SotA performance but also provides principled guidance for future ObjectNav development and evaluation.

Problem

• Identifying key components driving RL-based object navigation performance
• Analyzing perception, policy and test-time enhancement contributions systematically
• Proposing unified framework to bridge performance gap with humans

Innovation

• Analyzed perception policy and test-time enhancement
• Proposed practical design guidelines for navigation
• Enhanced modular system outperforms State-of-the-Art methods