SkillNav: スコアレベルスキル介入によるゼロショット物体目標ナビゲーション
SkillNav: Score-Level Skill Intervention for Zero-Shot Object Goal Navigation
VLMベースのナビゲーションエージェントに、地図上の好奇心価値マップを書き換える形で行動記憶を埋め込むスキルフレームワークを提案し、追加トークンなしでデッドエンドやループなどの失敗を軽減する。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Ruijie Sang, Yiqun Duan, Pinhan Fu, Ruilin Wang, Wei Sui, Xianda Guo
分類: cs.RO
原文アブストラクト
Vision-Language Model (VLM) agents have advanced zero-shot object-goal navigation, yet single-frame reasoning leaves them without the cross-step behavioral awareness an embodied navigator requires, producing recurring failures such as dead-end stalls, in-room loops, and circuitous approaches to detected targets. Prompt-based remedies inflate token budgets across multi-submodule episodes and still struggle to encode inherently spatial signals such as angles, map cells, and viewpoint coordinates. In this paper, we propose SkillNav, an extensible behavioral skill framework for VLM-based navigation that treats the curiosity value map already maintained by modern VLM navigators as a writable substrate on which composable skills inscribe behavioral memory at zero token cost. Skills are stratified into three tiers by their level of behavioral authority, namely soft scaling for proportional reweighting, lower-bound boost for region-level guarantees, and hard override for threshold-triggered forced actions, and cooperate across tiers under a fixed composition order that establishes a predictable, declared priority among skills. This design turns capability improvement into skill registration: new behaviors plug in without retraining the VLM or disturbing existing skills, opening a path for continual refinement. A minimal prompt channel complements the score-level skills with category-level semantic hints, yielding a dual-representation design in which spatial memory lives on the map and semantic memory in short prompts. Training-free, SkillNav establishes new state-of-the-art SPL across MP3D (25.5), HM3D v0.1 (39.3), and HM3D v0.2 (43.2), improving SPL by up to 6.0 absolute over the strongest prior method, and achieves the highest Success Rate on HM3D v0.1 (69.7) and v0.2 (75.9).
