Reinforcement Learning for Active Perception in Autonomous Navigation

Grzegorz Malczyk; Mihir Kulkarni; Kostas Alexis

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arXiv · 2602.01266

Reinforcement Learning for Active Perception in Autonomous Navigation

Grzegorz Malczyk·Mihir Kulkarni·Kostas Alexis

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Abstract

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.

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Grzegorz Malczyk, Mihir Kulkarni, Kostas Alexis. 2026-02-01. Reinforcement Learning for Active Perception in Autonomous Navigation. https://arxiv.org/abs/2602.01266

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