Reinforcement Learning for Active Perception in Autonomous Navigation

• URL: http://arxiv.org/abs/2602.01266v1
• Date: Sun, 01 Feb 2026 14:54:40 GMT
• Title: Reinforcement Learning for Active Perception in Autonomous Navigation
• Abstract summary: This paper addresses the challenge of active perception within autonomous navigation in complex, unknown environments.<n>We introduce an end-to-end reinforcement learning framework in which a robot must not only reach a goal but also actively control its onboard camera to enhance situational awareness.
• Score: 12.508044072300839
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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