TARIC: 遮断された意味的手がかり下でのメモリ拡張型・走行可能性認識屋外視覚言語ナビゲーション
TARIC: Memory-Augmented Traversability-Aware Outdoor VLN under Interrupted Semantic Cues
屋外視覚言語ナビゲーションにおいて、意味的手がかりが途切れる状況でも、走行可能性を考慮した実行可能なガイダンスを維持する統合フレームワークを提案。3Dキューメモリと不確実性を考慮した読み出し機構により、長期的な手がかり欠如フェーズでも安定したナビゲーションを実現する。
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著者: Tianle Zeng, Hanjing Ye, Jianwei Peng, Jingwen Yu, Hanxuan Chen, Hong Zhang
分類: cs.RO, cs.AI
原文アブストラクト
Outdoor vision-language navigation (VLN) in long-range, open-world environments is frequently disrupted by semantic-cue interruptions, where informative goal cues become sparse, occluded, or leave the field of view. Once such cues disappear, agents enter a cue-free phase and often degrade into backtracking, oscillatory headings, or aimless exploration. While memory-based methods attempt to bridge these gaps, they often fail under traversability-driven detours: the remembered cue direction may be infeasible, forcing detours that prolong cue-free phases and gradually render robot-centric cues stale and implicit histories blurred. This makes traversability a stability condition for maintaining goal-directed guidance, rather than merely a local safety concern. We propose a unified outdoor VLN framework that survives semantic-cue interruptions by maintaining traversability-consistent executable guidance throughout prolonged cue-free phases. Specifically, our method extracts semantic bearings from visibility-gated goal or exploration cues and grounds them into executable headings using a real-time near-field traversability profile, providing goal-consistent feasible guidance beyond reject-only safety filtering. To prevent guidance degradation during detours, we lift intermittent 2D evidence into a world-aligned 3D cue memory with an uncertainty-aware readout mechanism, ensuring guidance remains continuously reachable and stable as the robot moves. We evaluate the framework on quadrupedal and wheeled platforms over 600--1000 m routes. Our method improves simulation success rate by over 10 percentage points over the strongest baseline and achieves a real-world success rate of 40%, compared to 17.5% for the strongest baseline, with substantially higher robustness during prolonged cue-free intervals.
