OVAL: Open-Vocabulary Augmented Memory Model for Lifelong Object Goal Navigation
For embodied AI agents, this work addresses the limitation of lifelong memory in ObjectNav, enabling sustained navigation in open-vocabulary environments.
OVAL introduces a lifelong open-vocabulary memory framework for Object Goal Navigation, enabling efficient long-term navigation toward continual targets. Experiments show improved exploration efficiency and robustness over existing methods.
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.