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.

翻译：本文探讨了在复杂未知环境中自主导航的主动感知挑战。通过重新审视主动感知的基本原理，我们提出了一种端到端的强化学习框架，在该框架中，机器人不仅需要在避开障碍物的同时抵达目标，还需主动控制其机载相机以增强态势感知能力。策略接收的观测信息包括机器人状态、当前深度帧以及基于短期深度读数历史构建的局部几何表征。为了将无碰撞运动规划与信息驱动的主动相机控制相结合，我们在导航奖励中引入了基于体素的信息度量。这使得空中机器人能够学习一种鲁棒策略，在目标导向运动与探索性感知之间取得平衡。大量实验评估表明，相较于使用固定、非驱动的相机基线方法，我们的策略实现了更安全的飞行，同时诱发了内在的探索行为。