自律ナビゲーションにおける能動的知覚のための強化学習
Reinforcement Learning for Active Perception in Autonomous Navigation
未知環境での自律ナビゲーションにおいて、カメラを能動的に制御しながら目標到達と障害物回避を両立する強化学習フレームワークを提案。情報量指標を報酬に加えることで、探索的な知覚行動を獲得し、固定カメラより安全な飛行を実現した。
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著者: Grzegorz Malczyk, Mihir Kulkarni, Kostas Alexis
分類: cs.RO
原文アブストラクト
This paper addresses the challenge of active perception within autonomous navigation in complex, unknown environments. Revisiting the foundational principles of active perception, we introduce an end-to-end reinforcement learning framework in which a robot must not only reach a goal while avoiding obstacles, but also actively control its onboard camera to enhance situational awareness. The policy receives observations comprising the robot state, the current depth frame, and a particularly local geometry representation built from a short history of depth readings. To couple collision-free motion planning with information-driven active camera control, we augment the navigation reward with a voxel-based information metric. This enables an aerial robot to learn a robust policy that balances goal-directed motion with exploratory sensing. Extensive evaluation demonstrates that our strategy achieves safer flight compared to using fixed, non-actuated camera baselines while also inducing intrinsic exploratory behaviors.
