拓展你的SCOPE：基于潜在探索的语义认知用于具身视觉导航
Expand Your SCOPE: Semantic Cognition over Potential-Based Exploration for Embodied Visual Navigation
AI总结：
本文提出SCOPE框架，通过利用前沿信息驱动潜在探索，提升具身视觉导航中的决策质量与目标相关性，实验表明其在准确性和泛化能力上优于现有方法。
摘要：
具身视觉导航仍是一项具有挑战性的任务，因为智能体必须在有限知识下探索未知环境。现有零样本研究显示，结合记忆机制支持目标导向行为可提升长周期规划性能。然而，它们忽略了视觉前沿边界，这从根本上决定了未来轨迹和观察，且未能推断部分视觉观察与导航目标之间的关系。在本文中，我们提出了语义认知过潜在探索（SCOPE），一个零样本框架，明确利用前沿信息驱动潜在探索，使决策更加知情和目标相关。SCOPE利用视觉-语言模型估计探索潜力，并将其组织成时空潜力图，捕捉边界动态以支持长周期规划。此外，SCOPE还结合了自我反思机制，重新审视并优化先前决策，提高可靠性并减少过度自信的错误。在两个多样化的具身导航任务上的实验结果表明，SCOPE在准确性上比最先进的基线高出4.6%。进一步分析显示，其核心组件导致了更好的校准、更强的泛化能力和更高的决策质量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
