# Reinforcement Learning for Active Perception in Autonomous Navigation

arXiv: https://arxiv.org/abs/2602.01266 · HTML: https://mlsanity.com/paper/2602.01266/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 1 Feb 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 14 of 14)

- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [Learning to Navigate in Complex Environments](https://mlsanity.com/paper/1611.03673/) (2016)
- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [Revisiting Active Perception](https://mlsanity.com/paper/1603.02729/) (2016)
- [Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning](https://mlsanity.com/paper/2006.11751/) (2020)
- [Back to Newton's Laws: Learning Vision-based Agile Flight via Differentiable Physics](https://mlsanity.com/paper/2407.10648/) (2024)
- [Aerial Gym Simulator: A Framework for Highly Parallelized Simulation of Aerial Robots](https://mlsanity.com/paper/2503.01471/) (2025)
- [Reinforcement Learning for Collision-free Flight Exploiting Deep Collision Encoding](https://mlsanity.com/paper/2402.03947/) (2024)
- [Task-driven Compression for Collision Encoding based on Depth Images](https://mlsanity.com/paper/2309.05289/) (2023)
- [Semantically-driven Deep Reinforcement Learning for Inspection Path Planning](https://mlsanity.com/paper/2505.14443/) (2025)
- [Quadrotor Navigation using Reinforcement Learning with Privileged Information](https://mlsanity.com/paper/2509.08177/) (2025)
- [Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning](https://mlsanity.com/paper/2209.06336/) (2022)

## Built on by (most cited first, 4 of 4)

- [Fast Human Attention Prediction for Fixation-guided Active Perception in Autonomous Navigation](https://mlsanity.com/paper/2606.20491/) (2026)
- [LEAP: Learning Emergent Active Perception for Quadruped Navigation](https://mlsanity.com/paper/2609.17628/) (2026)
- [UAVs Meet Embodied Intelligence: Bridging Human Intents and Flying Dynamics Via Harnessing Physical-Digital AI Agents](https://mlsanity.com/paper/2609.18326/) (2026)
- [ClearNav: Perception-Aligned Quadrotor Flight Using Differential-Physics](https://mlsanity.com/paper/2610.17419/) (2026)

## Abstract (arXiv)

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.