提出OVAL框架，让机器人长期记住并找到新目标物。
OVAL: Open-Vocabulary Augmented Memory Model for Lifelong Object Goal Navigation
- 用记忆描述符结构化管理长期记忆，支持开放词汇
- 多值前沿评分策略提升持续探索效率
- 适合需要长期记忆与新目标识别的导航任务
物体目标导航（ObjectNav）指智能体在未见过的环境中寻找到特定物体，这是完成复杂任务的关键能力。现有方法在单一物体导航上表现良好，但在长期记忆表示上的局限性导致其难以持续应对新目标。为此，我们提出OVAL——一种新型的终身开放词汇记忆框架，实现语义开放任务下的高效精准长时导航。该框架引入记忆描述符以结构化管理记忆模型，并提出基于概率的探索策略，利用多值前沿评分机制提升长期探索效率。大量实验表明，所提系统具有良好的效率与鲁棒性。
原文摘要 · Abstract (English)
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.
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