OVAL: 开放词汇增强记忆模型用于终身物体目标导航
OVAL: Open-Vocabulary Augmented Memory Model for Lifelong Object Goal Navigation
AI总结：
OVAL提出一种开放词汇增强记忆框架，解决长期目标导航中的记忆限制问题，通过内存描述符和概率探索策略提升导航效率与鲁棒性。
摘要：
物体目标导航（ObjectNav）指的是智能体在未见过的环境中导航至特定物体，这是完成复杂任务所需的能力。尽管现有方法在单一物体导航上表现优异，但其在终身记忆表示的有限适用性限制了对连续目标的长期导航效果。为此，我们提出了OVAL，一种新的终身开放词汇记忆框架，能够高效且精确地执行语义开放任务中的长期导航。在此框架中，我们引入了内存描述符以促进记忆模型的结构化管理。此外，我们提出了一种基于概率的探索策略，利用多值前沿评分来增强终身探索效率。大量实验验证了所提系统的效率和鲁棒性。
英文摘要：
Object Goal Navigation (ObjectNav) refers to an agent navigating to an object in an unseen environment, which is an ability often required in the accomplishment of complex tasks. While existing methods demonstrate proficiency in isolated single object navigation, their limitations emerge in the restricted applicability of lifelong memory representations, which ultimately hinders effective navigation toward continual targets over extended periods. To address this problem, we propose OVAL, a novel lifelong open-vocabulary memory framework, which enables efficient and precise execution of long-term navigation in semantically open tasks. Within this framework, we introduce memory descriptors to facilitate structured management of the memory model. Additionally, we propose a novel probability-based exploration strategy, utilizing a multi-value frontier scoring to enhance lifelong exploration efficiency. Extensive experiments demonstrate the efficiency and robustness of the proposed system.
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。
