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

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 costbenefit 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.