Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation

• URL: http://arxiv.org/abs/2402.17587v3
• Date: Fri, 22 Mar 2024 07:23:51 GMT
• Title: Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation
• Authors: Xiaohan Lei, Min Wang, Wengang Zhou, Li Li, Houqiang Li,
• Abstract summary: Instance ImageGoal Navigation (IIN) aims to navigate to a specified object depicted by a goal image in an unexplored environment. We propose a new modular navigation framework named Instance-aware Exploration-Verification-Exploitation (IEVE) for instance-level image goal navigation. Our method surpasses previous state-of-the-art work, with a classical segmentation model (0.684 vs. 0.561 success) or a robust model (0.702 vs. 0.561 success)
• Score: 88.84058353659107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a new embodied vision task, Instance ImageGoal Navigation (IIN) aims to navigate to a specified object depicted by a goal image in an unexplored environment. The main challenge of this task lies in identifying the target object from different viewpoints while rejecting similar distractors. Existing ImageGoal Navigation methods usually adopt the simple Exploration-Exploitation framework and ignore the identification of specific instance during navigation. In this work, we propose to imitate the human behaviour of ``getting closer to confirm" when distinguishing objects from a distance. Specifically, we design a new modular navigation framework named Instance-aware Exploration-Verification-Exploitation (IEVE) for instance-level image goal navigation. Our method allows for active switching among the exploration, verification, and exploitation actions, thereby facilitating the agent in making reasonable decisions under different situations. On the challenging HabitatMatterport 3D semantic (HM3D-SEM) dataset, our method surpasses previous state-of-the-art work, with a classical segmentation model (0.684 vs. 0.561 success) or a robust model (0.702 vs. 0.561 success)

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