Expand Your SCOPE: Semantic Cognition over Potential-Based Exploration for Embodied Visual Navigation

2025-11-12

AI Summary

Existing zero-shot embodied visual navigation methods neglect the influence of visual boundaries on trajectory planning and struggle to model semantic relationships between local observations and navigation goals. To address these limitations, we propose a navigation framework that synergistically integrates semantic cognition with potential-field-based exploration: (1) a vision-language model estimates regional exploration potential to construct spatiotemporal potential maps; (2) a memory-augmented mechanism coupled with a self-reassessment strategy dynamically refines decision-making. Our approach explicitly incorporates visual boundary constraints and enhances goal-directed, long-horizon planning capability. Evaluated on two embodied navigation benchmarks, our method achieves a 4.6% absolute accuracy improvement over prior state-of-the-art methods. Ablation studies validate the effectiveness of potential-driven planning, semantic-potential coupling, and the self-reassessment mechanism.

Technology Category

• Computer Vision: Language and Vision
• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Embodied visual navigation remains a challenging task, as agents must explore unknown environments with limited knowledge. Existing zero-shot studies have shown that incorporating memory mechanisms to support goal-directed behavior can improve long-horizon planning performance. However, they overlook visual frontier boundaries, which fundamentally dictate future trajectories and observations, and fall short of inferring the relationship between partial visual observations and navigation goals. In this paper, we propose Semantic Cognition Over Potential-based Exploration (SCOPE), a zero-shot framework that explicitly leverages frontier information to drive potential-based exploration, enabling more informed and goal-relevant decisions. SCOPE estimates exploration potential with a Vision-Language Model and organizes it into a spatio-temporal potential graph, capturing boundary dynamics to support long-horizon planning. In addition, SCOPE incorporates a self-reconsideration mechanism that revisits and refines prior decisions, enhancing reliability and reducing overconfident errors. Experimental results on two diverse embodied navigation tasks show that SCOPE outperforms state-of-the-art baselines by 4.6% in accuracy. Further analysis demonstrates that its core components lead to improved calibration, stronger generalization, and higher decision quality.

Problem

• Agents explore unknown environments with limited visual knowledge
• Existing methods overlook visual frontier boundaries and goal relationships
• Current approaches lack mechanisms to refine prior navigation decisions

Innovation

• Leverages frontier information for potential-based exploration
• Uses Vision-Language Model to estimate exploration potential
• Incorporates self-reconsideration mechanism to refine decisions