不整地を移動障害物とともに歩くヒューマノイド向け身体性接地型VLMナビゲーション
HINT-VLM: Humanoid Interactive Navigation over Rough Terrain with Embodiment-Grounded Vision-Language Model
VLMの意味的な可動性予測をガウス過程で身体性に接地させ、不整地での移動障害物を考慮した階層的な不確実性対応計画を実現したヒューマノイドナビゲーション手法。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Ziwon Yoon, Seok Hwan Kim, Yiyu Chen, Quan Nguyen, Lu Gan, Ye Zhao
分類: cs.RO
原文アブストラクト
Humanoid robots have achieved significant progress in locomotion and manipulation, enabling traversal over rough terrain and richer interaction with the environment, such as moving obstacles. However, most traditional navigation methods assume non-interactive environments and focus only on traversal. While Navigation Among Movable Obstacles (NAMO) approaches relax this assumption, prior efforts have largely focused on flat environments and reactive interaction, rather than predicting object movability and planning ahead. These limitations become critical in outdoor rough-terrain scenarios, where robots must jointly reason about terrain traversability and object movability. In this work, we address NAMO for humanoid robots in cluttered outdoor rough terrain by proposing a hybrid planning framework that combines semantic priors from a Vision-Language Model (VLM) with embodied robot interaction data through a Gaussian process (GP) residual model. The GP corrects the VLM-derived movability prior to produce embodiment-grounded probabilistic movability estimates, which are then incorporated into hierarchical uncertainty-aware planning. The proposed approach allows humanoids to jointly leverage locomotion and manipulation for efficient and safe navigation, representing the first solution to NAMO in rough terrain through predictive movability reasoning and probabilistic VLM integration.
