RATE -Nav: Region-Aware Termination Enhancement for Zero-shot Object Navigation with Vision-Language Models (2025)

Abstract: Object Navigation (ObjectNav) is a fundamental task in embodied artificial intelligence. Although significant progress has been made in semantic map construction and target direction prediction in current research, redundant exploration and exploration failures remain inevitable. A critical but underexplored direction is the timely termination of exploration to overcome these challenges. We observe a diminishing marginal effect between exploration steps and exploration rates and analyze the cost-benefit relationship of exploration. Inspired by this, we propose RATE-Nav, a Region-Aware Termination-Enhanced method. It includes a geometric predictive region segmentation algorithm and region-Based exploration estimation algorithm for exploration rate calculation. By leveraging the visual question answering capabilities of visual language models (VLMs) and exploration rates enables efficient termination.RATE-Nav achieves a success rate of 67.8% and an SPL of 31.3% on the HM3D dataset. And on the more challenging MP3D dataset, RATE-Nav shows approximately 10% improvement over previous zero-shot methods.

## introduction object navigation @xcite , as one of the core capabilities of embodied agents, aims to enable agents to autonomously locate and navigate to specified target objects in unknown environments @xcite . this capability is crucial for practical applications such as service robots, for instance, in performing fetch tasks in home environments. however, in real-world applications, robots often operate in unknown and dynamically changing environments, and search targets are not limited to predefined categories, posing significant † jianzong wang and guokuan li are joint corresponding authors. figure 1 : the main difference between our method and existing approaches is the exploration strategy. naive agent fully explores the current region before moving to the next. in contrast, our agent uses the commonsense reasoning of vlms to decide whether to continue exploring the current region after achieving a certain exploration level. with the rapid development of large language models (llms) @xcite and vision-language models (vlms) @xcite @xcite ) with powerful reasoning capabilities in the field of artificial intelligence, researchers @xcite have turned to employing these models for intermediate goal planning: first, by modeling environmental observations with vlms to construct semantically rich scene representations, and then utilizing the commonsense reasoning abilities embedded in llms to predict the possible locations of target objects. methods @xcite based on llms and vlms have demonstrated notable advantages navigation strategies, thereby enhancing the performance of navigation systems to some extent. existing research relies on comprehensive exploration, but there remains a research gap in the design of exploration termination strategies. as shown in figure 1 , traditional navigation strategies struggle to accurately assess the current exploration state when searching specific areas, typically requiring a complete search of the current area before moving to a new one. from a marginal utility perspective, this strategy is inefficient: in the early stages of information gathering, the investment yields significant returns, but as exploration deepens, the marginal value of new information gained from each operation gradually diminishes.this prompts us to consider whether the search in the current area could be terminated earlier. to validate this hypothesis, we conducted hundreds of navigation experiments on the hm3d dataset, thoroughly analyzing the relationship between exploration cost and benefit. detailed analysis can be found in chapter 3.1. furthermore, existing research primarily relies on exploration maps constructed by visual perception to identify target-free regions. precision errors and model limitations make it difficult to fully annotate these regions on the map. as shown in figure 3 , a situation may occur where a region is largely explored, but repeated boundary settings are triggered due to a small unknown area. this results in redundant exploration, which not only wastes resources but also reduces the adaptability and flexibility of the navigation system. this inspires us to consider whether we can estimate explored region to avoid repeated exploration and thereby improve efficiency. based on our analysis, we propose rate-nav (region-aware termination-enhanced navigation), a novel navigation method that enhances exploration efficiency through region-level search and intelligent termination strategies. as one of its key components, we develop a geometric predictive region segmentation module that assists in partitioning incomplete environmental maps into relatively independent regions. this segmentation module utilizes geometric features to predict unexplored areas, contributing to the transformation from point-by-point search into a regionlevel search problem. the region-based approach enables holistic evaluation of environmental segments, leading to better estimation of unknown spaces and improved navigation efficiency. for the critical task of region-level evaluation, we leverage vlms which excel at macroscopic environmental perception. when vlms determine that a target object is unlikely to exist within a region, the sys-tem designates it as an "exploration-free zone," immediately terminating current exploration and preventing future redundant searches in that area. this vlm-based termination strategy effectively reduces unnecessary exploration and substantially improves overall efficiency. our contributions are summarized as follows: ## methodology 4.1 method overview to address the challenges mentioned in chapter 3.2, we propose a macro-perception strategy that leverages vlms and region exploration rate to evaluate the potential of continuous exploration, enabling efficient exploration termination. the rate-nav workflow is illustrated in figure 3 and algorithm 1. our geometric predictive region segmentation algorithm utilizes geometric features to predict and segment unexplored areas within the map, providing a robust solution to the challenge of ambiguous region references in vlm outputs. this geometric feature-based approach enables more accurate and meaningful region partitioning. following region segmentation, we introduce a method for estimating explored area that considers both the robot's visible areas and traversable spaces. when the marginal utility of exploring a specific region drops below a threshold (indicated by the region exploration rate), we filter key frame inputs from rgb observations and feed them into the vlm for macro-environmental perception. the vlm's logical reasoning capabilities then determine whether to terminate further exploration of that region, preventing redundant exploration in low-value areas. ## conclusion in this paper, we introduced rate-nav, a novel navigation method based on the law of diminishing returns. our geometric predictive region segmentation method predicts unexplored areas using geometric features for region partitioning. converting point-to-point to region-based processing with vlm spatial reasoning improves navigation accuracy and efficiency. while our method effectively handles region-based references, future work should focus on enhancing spatial understanding for terms like "ahead" or "to the right" to enable more natural vision-language navigation.