WMNav: 視覚言語モデルを世界モデルに統合した物体目標ナビゲーション
WMNav: Integrating Vision-Language Models into World Models for Object Goal Navigation
視覚言語モデルを用いて世界モデルを構築し、将来状態を予測しながら未知環境で目標物体を探索するナビゲーション手法を提案。HM3DとMP3Dでゼロショットベースラインを上回る性能を達成。
著者: Dujun Nie, Xianda Guo, Yiqun Duan, Ruijun Zhang, Long Chen
分類: cs.CV, cs.RO
原文アブストラクト
Object Goal Navigation-requiring an agent to locate a specific object in an unseen environment-remains a core challenge in embodied AI. Although recent progress in Vision-Language Model (VLM)-based agents has demonstrated promising perception and decision-making abilities through prompting, none has yet established a fully modular world model design that reduces risky and costly interactions with the environment by predicting the future state of the world. We introduce WMNav, a novel World Model-based Navigation framework powered by Vision-Language Models (VLMs). It predicts possible outcomes of decisions and builds memories to provide feedback to the policy module. To retain the predicted state of the environment, WMNav proposes the online maintained Curiosity Value Map as part of the world model memory to provide dynamic configuration for navigation policy. By decomposing according to a human-like thinking process, WMNav effectively alleviates the impact of model hallucination by making decisions based on the feedback difference between the world model plan and observation. To further boost efficiency, we implement a two-stage action proposer strategy: broad exploration followed by precise localization. Extensive evaluation on HM3D and MP3D validates WMNav surpasses existing zero-shot benchmarks in both success rate and exploration efficiency (absolute improvement: +3.2% SR and +3.2% SPL on HM3D, +13.5% SR and +1.1% SPL on MP3D). Project page: https://b0b8k1ng.github.io/WMNav/.
