GUIDE: 目標初期化による方向理解を用いたエンドツーエンド視覚ナビゲーション
GUIDE: Goal-Initialized Directional Understanding for End-to-End Visual Navigation
脚式ロボットのナビゲーションにおいて、目標をエピソード開始時に一度だけ与え、外部からの目標更新なしに内部の空間記憶と方向認識だけで動作するエンドツーエンドの強化学習フレームワークGUIDEを提案した。
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著者: Liang Wang, Jin Jin, KanZhong Yao, YiBin Wu, Fangqiang Ding, Jin Wang, Jun Wu, Zhe Sun, Qiuguo Zhu
分類: cs.RO
原文アブストラクト
Learning-based visual navigation for legged robots typically relies on continuous goal updates from hierarchical state estimation to provide a persistent directional reference. This reliance incurs additional sensory and computational overhead and deviates from fully end-to-end mobile autonomy. Furthermore, under partial observability, policies are prone to learn myopic behaviors, easily becoming trapped in dead ends and complex structural layouts. To address these limitations, we investigate a goal-initialized navigation setting, where the target is provided only once at the beginning of an episode, requiring the robot to operate based on intrinsic spatial memory without subsequent goal updates from external modules. In this work, we propose GUIDE, a fully end-to-end reinforcement learning framework designed to cultivate internal directional awareness. Specifically, GUIDE incorporates a spatial anchor predictor that leverages multi-frequency proprioceptive history to extract egomotion representations, thereby maintaining a persistent long-horizon spatial context for navigation. Concurrently, it utilizes raw depth streams to perceive local environmental geometry. We evaluate the proposed framework across both simulation and real-world scenarios on a quadruped robot. Experiments show that GUIDE learns reliable egomotion and directional awareness, enabling a fully end-to-end deployed policy to safely navigate through dense clutter and structured mazes without subsequent goal guidance or prior maps.
