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.

翻译：物体目标导航（ObjectNav）是指智能体在未知环境中导航至指定物体的能力，这通常是完成复杂任务所需的关键技能。现有方法在孤立单物体导航中表现良好，但其终身记忆表征的适用性受限，最终阻碍了长时间内对连续目标的有效导航。为解决这一问题，我们提出OVAL——一种新颖的终身开放词汇记忆框架，能够在语义开放的任务中实现高效、精准的长期导航。在该框架内，我们引入记忆描述符以促进记忆模型的结构化管理。此外，我们提出一种基于概率的新型探索策略，利用多值前沿评分机制提升终身探索效率。大量实验证明了所提系统的有效性与鲁棒性。