AgenticNav: ツール呼び出しハーネスとしてのゼロショット視覚言語ナビゲーション
AgenticNav: Zero-Shot Vision-and-Language Navigation as a Tool-Calling Harness
連続環境でのゼロショット視覚言語ナビゲーションを、VLMが行動・深度・記憶をツールとして呼び出せるエージェント的インターフェースとして再設計し、SOTA性能を達成した。
詳しい要約
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著者: Yijian Li, Changze Li, Hantian Shi, Jiaying Luo, Jiyuan Cai, Ming Yang, Tong Qin
分類: cs.RO
原文アブストラクト
Zero-shot vision-and-language navigation in continuous environments (VLN-CE) has recently become feasible with large vision-language models (VLMs). However, existing methods typically rely on learned waypoint predictors to propose navigable actions. This severely limits the model's action space and fails to leverage depth inputs effectively. Moreover, memory is commonly handled by accumulating long textual or visual histories with substantial irrelevant context, or by retrieving cross-episode experiences, which weakens the zero-shot setting. In this paper, we rethink zero-shot VLN-CE as an agentic interface between the VLM and the environment, and present AgenticNav, a lightweight navigation harness that exposes action, depth, and memory as callable tools. Instead of choosing from predicted waypoints, the action tool allows the VLM to directly select a target pixel in RGB observations, converting it into executable motion. Depth is exposed through an on-demand pixel-depth tool, enabling the VLM to request precise metric distances only where they matter. For memory, AgenticNav provides a compact map image summarizing the historical trajectory, paired with a recall tool that allows the VLM to selectively revisit past visual observations without overwhelming the prompt context. On the R2R-CE benchmark, AgenticNav establishes new state-of-the-art (SOTA) performance among zero-shot methods given the same VLM backbone. Real-world validation further highlights its zero-shot generalization compared to prior methods. Ablations show that our action tool design outperforms traditional waypoint predictors, and that depth tool and agentic memory further contribute to navigation performance.
