Room-Object Entity Prompting and Reasoning for Embodied Referring Expression

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE(2024)

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摘要
Given a high-level instruction, the task of Embodied Referring Expression (REVERIE) requires an embodied agent to localise a remote referred object via navigating in the unseen environment. Previous vision-language navigation methods utilise the provided fine-grained instruction as step-by-step navigation guidance to conduct strict instruction-following, while REVERIE aims to achieve efficient goal-oriented exploration according to the high-level command. In this work, we propose a Cross-modal Knowledge Reasoning (abbreviated as CKR+) framework, which incorporates the prior knowledge as decision guidance to learn the navigation scheme comprehensively. Specifically, we design a Room-Object Aware (ROA) mechanism to explicitly decouple the room- and object-related clues from instruction and visual observations. Moreover, we propose a Knowledge-enabled Entity Relation Reasoning (KERR+) module to leverage the structured knowledge from the knowledge graph explicitly and unstructured knowledge from pre-trained model implicitly, to learn the internal-external correlations among room- and object-entities for the agent to make proper decisions. We devise an Entity Prompter (EP) that embeds in the KERR+ module, which utilises the navigation history and visual entities as prompts to transfer knowledge from the pre-trained CLIP model. In addition, we develop a Reinforced End Decider (RED) to learn the stopping scheme specifically, which is achieved by a customised reinforcement learning strategy and knowledge enhanced matching. Two techniques are also introduced to improve navigation efficiency further. Extensive experiments conducted on the REVERIE benchmark demonstrate the effectiveness and superiority of our proposed methods, which boosts the key metrics, i.e., SPL and REVERIE-success rate, to 14.46% and 13.81% respectively.
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关键词
Embodied-AI,instruction-based navigation,cross-modal learning,entity prompting,knowledge reasoning
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