PanoDP: パノラマ深度と微分可能物理を用いた衝突回避ナビゲーション学習
PanoDP: Learning Collision-Free Navigation with Panoramic Depth and Differentiable Physics
4方向のパノラマ深度画像と微分可能な物理シミュレーションを組み合わせ、密集環境での衝突回避ナビゲーションを学習するフレームワークを提案した。
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著者: Hao Zhong, Pei Chi, Jiang Zhao, Shenghai Yuan, Xuyang Gao, Thien-Minh Nguyen, Lihua Xie
分類: cs.RO
原文アブストラクト
Autonomous collision-free navigation in cluttered environments requires safe decision-making under partial observability with both static structure and dynamic obstacles. We present \textbf{PanoDP}, a communication-free learning framework that combines four-view panoramic depth perception with differentiable-physics-based training signals. PanoDP encodes panoramic depth using a lightweight CNN and optimizes policies with dense differentiable collision and motion-feasibility terms, improving training stability beyond sparse terminal collisions. We evaluate PanoDP on a controlled ring-to-center benchmark with systematic sweeps over agent count, obstacle density/layout, and dynamic behaviors, and further test out-of-distribution generalization in an external simulator (e.g., AirSim). Across settings, PanoDP increases collision-free and completion rates over single-view and non-physics-guided baselines under matched training budgets, and ablations (view masking, rotation augmentation) confirm the policy leverages 360-degree information. Code will be open source upon acceptance.
