STRIDER: 指示に整合した構造的決定空間最適化によるナビゲーション
STRIDER: Navigation via Instruction-Aligned Structural Decision Space Optimization
未見の3D環境を自然言語指示でナビゲートするゼロショットVLN-CEにおいて、空間構造の事前知識とタスク進捗フィードバックを組み合わせて決定空間を最適化し、成功率を29%から35%へ向上させたフレームワーク。
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著者: Diqi He, Xuehao Gao, Hao Li, Junwei Han, Dingwen Zhang
分類: cs.RO, cs.AI
原文アブストラクト
The Zero-shot Vision-and-Language Navigation in Continuous Environments (VLN-CE) task requires agents to navigate previously unseen 3D environments using natural language instructions, without any scene-specific training. A critical challenge in this setting lies in ensuring agents' actions align with both spatial structure and task intent over long-horizon execution. Existing methods often fail to achieve robust navigation due to a lack of structured decision-making and insufficient integration of feedback from previous actions. To address these challenges, we propose STRIDER (Instruction-Aligned Structural Decision Space Optimization), a novel framework that systematically optimizes the agent's decision space by integrating spatial layout priors and dynamic task feedback. Our approach introduces two key innovations: 1) a Structured Waypoint Generator that constrains the action space through spatial structure, and 2) a Task-Alignment Regulator that adjusts behavior based on task progress, ensuring semantic alignment throughout navigation. Extensive experiments on the R2R-CE and RxR-CE benchmarks demonstrate that STRIDER significantly outperforms strong SOTA across key metrics; in particular, it improves Success Rate (SR) from 29% to 35%, a relative gain of 20.7%. Such results highlight the importance of spatially constrained decision-making and feedback-guided execution in improving navigation fidelity for zero-shot VLN-CE.
