Reinforcement Learning for Active Perception in Autonomous Navigation

2026-02-01

AI Summary

This work addresses the challenge of simultaneously ensuring safe obstacle avoidance and efficient environmental perception for autonomous aerial robots operating in complex, unknown environments. The authors propose an end-to-end reinforcement learning framework that actively controls the onboard camera to jointly optimize navigation and information gathering by fusing robot state, depth images, and local geometric representations. A key innovation lies in embedding a voxelized information metric directly into the reward function, enabling, for the first time, the joint learning of goal-directed motion and exploratory perception, thereby eliciting intrinsic exploratory behavior. Experimental results demonstrate that the proposed method significantly improves both flight safety and environmental exploration efficiency compared to fixed-viewpoint baselines.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• active perception
• autonomous navigation
• reinforcement learning
• camera control
• situational awareness

Innovation

• active perception
• reinforcement learning
• autonomous navigation
• voxel-based information metric
• end-to-end learning