人間のように探索する：身体化エージェントのためのオンラインSGメモ構築による自律探索
Explore Like Humans: Autonomous Exploration with Online SG-Memo Construction for Embodied Agents
本論文は、大規模視覚言語モデルを用いて意味的ナビゲーション可能性を抽出し、階層的なSGメモをオンラインで構築しながら探索を行うことで、効率的な環境探索を実現するフレームワークABot-Explorerを提案する。
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著者: Xu Chen, Shichao Xie, Zhining Gu, Lu Jia, Minghua Luo, Fei Liu, Zedong Chu, Yanfen Shen, Xiaolong Wu, Mu Xu
分類: cs.CV
原文アブストラクト
Constructing structured spatial memory is essential for enabling long-horizon reasoning in complex embodied navigation tasks. Current memory construction predominantly relies on a decoupled, two-stage paradigm: agents first aggregate environmental data through exploration, followed by the offline reconstruction of spatial memory. However, this post-hoc and geometry-centric approach precludes agents from leveraging high-level semantic intelligence, often causing them to overlook navigationally critical landmarks (e.g., doorways and staircases) that serve as fundamental semantic anchors in human cognitive maps. To bridge this gap, we propose ABot-Explorer, a novel active exploration framework that unifies memory construction and exploration into an online, RGB-only process. At its core, ABot-Explorer leverages Large Vision-Language Models (VLMs) to distill Semantic Navigational Affordances (SNA), which act as cognitive-aligned anchors to guide the agent's movement. By dynamically integrating these SNAs into a hierarchical SG-Memo, ABot-Explorer mirrors human-like exploratory logic by prioritizing structural transit nodes to facilitate efficient coverage. To support this framework, we contribute a large-scale dataset extending InteriorGS with SNA and SG-Memo annotations. Experimental results demonstrate that ABot-Explorer significantly outperforms current state-of-the-art methods in both exploration efficiency and environment coverage, while the resulting SG-Memo is shown to effectively support diverse downstream tasks.
