ウェイポイントを超えて：クロスエンボディメント意味的ナビゲーションのためのデュアルヒートマップ接地
Beyond Waypoints: Dual-Heatmap Grounding for Cross-Embodiment Semantic Navigation
オープンエンドな意味的指示を物理的に実行可能な局所目標に変換する際、単一点回帰の代わりに到達可能領域と向き制約を表すデュアルヒートマップを予測するVLAフレームワークを提案し、多様なロボットでの性能向上を示した。
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著者: Kaijie Yun, Yue Chen
分類: cs.RO
原文アブストラクト
Grounding open-ended semantic instructions into physically executable local goals is a fundamental challenge in human-robot interaction. While existing navigation frameworks often regress deterministic waypoints, this rigid formulation collapses spatial uncertainty and frequently targets non-traversable object centers, leading to severe execution failures. In this work, we focus on the practical setting of in-FOV semantic navigation, where a robot receives concise, interleaved multimodal (text and image) prompts. To bridge the gap between abstract semantic intent and physical reachability, we propose a unified Vision-Language framework that abandons single-point regression in favor of a Dual-Heatmap representation. Our framework predicts a navigation affordance heatmap that captures continuous reachable regions, coupled with a facing heatmap for orientation constraints. These dense outputs inherently function as a differentiable semantic potential field, integrating seamlessly with downstream local planners. To support this paradigm, we build a fully automated, foundation-model-assisted synthetic data pipeline and establish a comprehensive simulation benchmark. Extensive experiments demonstrate that our framework achieves state-of-the-art performance among comparable 8B baselines. Crucially, a feature-fusion study and simulation studies across diverse robot embodiments (Jetbot, H1, Aliengo) reveal that explicit heatmap prediction drastically improves the Affordance Rate (AR). By placing targets reliably in executable free space, our framework effectively mitigates the brittleness of point regression, offering a transferable path toward safe cross-embodiment semantic navigation.
