ルートからステップへ：視覚言語ナビゲーションにおける意味的進捗と局所実行の分離
From Routes to Steps: Separating Semantic Progress from Local Execution in Vision-and-Language Navigation
視覚言語ナビゲーションにおいて、ルート全体の指示と局所的な行動生成を分離するフレームワークRoute2Stepを提案し、進捗追跡の誤りと実行の誤りを区別して学習する。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Xiangyun Huang, Xiangchen Wang, Runfeng Lin, Yihao Xu, Kangyu Huang, Jiang Hengchen, Xiwang Dong, Lin Jiarong
分類: cs.CV, cs.RO
原文アブストラクト
Vision-and-Language Navigation (VLN) requires an agent to follow a route-level instruction by executing its constituent steps from egocentric visual observations. Existing VLM-based navigators typically supervise both capabilities through next-action prediction alone, making progress-tracking errors difficult to distinguish from execution errors. When an agent deviates from the route, a corrective action label may recover the next movement but does not indicate whether the agent selected the wrong sub-instruction or failed to execute the correct one. Consequently, the agent may continue making decisions from an erroneous progress state. To resolve this ambiguity, we propose \textbf{Route2Step}, a framework that decouples semantic progress tracking from action generation through an explicit step-level interface. The Instruction Analysis Module ($\mathcal{M}_{\mathrm{IA}}$) predicts this state from the global instruction and visual history. Conditioned on the predicted state and recent observations, the Action Generation Module ($\mathcal{M}_{\mathrm{AG}}$) generates local action chunks. To supervise the progress state without manual temporal labels, E-SPA, a step-alignment procedure, associates sub-instructions with their corresponding portions of route-level demonstrations. These alignments enable state supervision for incorrect progress estimates, while direct action supervision is reserved for rollout groups that repeatedly fail under the correct active sub-instruction. On R2R-CE, Route2Step improves SR from 48.1\% to 55.3\% and SPL from 43.3\% to 48.2\%, using 190K state-level corrective samples while requiring only 11.5K directly action-supervised states. Experiments in real-world indoor and outdoor environments further demonstrate the practical applicability of Route2Step. The project page is: https://sisyphus-hxy.github.io/Route2Step/.
