# MAVRL: Learn to Fly in Cluttered Environments with Varying Speed

arXiv: https://arxiv.org/abs/2402.08381 · HTML: https://mlsanity.com/paper/2402.08381/

## Key facts

- Cited by 13 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 18 of its 28 references that are in the map.
- First posted on arXiv 13 Feb 2024.

## 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, 18 of 18)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [CAD2RL: Real Single-Image Flight without a Single Real Image](https://mlsanity.com/paper/1611.04201/) (2016)
- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [BADGR: An Autonomous Self-Supervised Learning-Based Navigation System](https://mlsanity.com/paper/2002.05700/) (2020)
- [Flightmare: A Flexible Quadrotor Simulator](https://mlsanity.com/paper/2009.00563/) (2020)
- [Robot Motion Planning in Learned Latent Spaces](https://mlsanity.com/paper/1807.10366/) (2018)
- [Learning to Fly by Crashing](https://mlsanity.com/paper/1704.05588/) (2017)
- [Learning a State Representation and Navigation in Cluttered and Dynamic Environments](https://mlsanity.com/paper/2103.04351/) (2021)
- [Agilicious: Open-Source and Open-Hardware Agile Quadrotor for Vision-Based Flight](https://mlsanity.com/paper/2307.06100/) (2023)
- [Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV with Limited Environment Knowledge](https://mlsanity.com/paper/1811.03307/) (2018)
- [Learning Perception-Aware Agile Flight in Cluttered Environments](https://mlsanity.com/paper/2210.01841/) (2022)
- [Motion Primitives-based Navigation Planning using Deep Collision Prediction](https://mlsanity.com/paper/2201.03254/) (2022)
- [AvoidBench: A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors](https://mlsanity.com/paper/2301.07430/) (2023)
- [Semantically-enhanced Deep Collision Prediction for Autonomous Navigation using Aerial Robots](https://mlsanity.com/paper/2307.11522/) (2023)
- [Task-driven Compression for Collision Encoding based on Depth Images](https://mlsanity.com/paper/2309.05289/) (2023)
- [SACPlanner: Real-World Collision Avoidance with a Soft Actor Critic Local Planner and Polar State Representations](https://mlsanity.com/paper/2303.11801/) (2023)

## Built on by (most cited first, 13 of 13)

- [Learning Speed Adaptation for Flight in Clutter](https://mlsanity.com/paper/2403.04586/) (2024)
- [SkyDreamer: Interpretable End-to-End Vision-Based Drone Racing with Model-Based Reinforcement Learning](https://mlsanity.com/paper/2510.14783/) (2025)
- [Flying on Point Clouds with Reinforcement Learning](https://mlsanity.com/paper/2503.00496/) (2025)
- [RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation](https://mlsanity.com/paper/2502.02054/) (2025)
- [Depth Transfer: Learning to See Like a Simulator for Real-World Drone Navigation](https://mlsanity.com/paper/2505.12428/) (2025)
- [AirDreamer: Generalist Drone Navigation with World Models](https://mlsanity.com/paper/2606.03252/) (2026)
- [Towards Task-Oriented Flying: Framework, Infrastructure, and Principles](https://mlsanity.com/paper/2504.15129/) (2025)
- [High-Speed Vision-Based Flight in Clutter with Safety-Shielded Reinforcement Learning](https://mlsanity.com/paper/2602.08653/) (2026)
- [Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles](https://mlsanity.com/paper/2602.24030/) (2026)
- [Zero-Shot UAV Navigation in Forests via Relightable 3D Gaussian Splatting](https://mlsanity.com/paper/2602.07101/) (2026)
- [CMRL: Collision-Aware and Memory-Enhanced Reinforcement Learning for UAV Navigation in Multi-Scale Obstacle Environments](https://mlsanity.com/paper/2605.14810/) (2026)
- [Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments](https://mlsanity.com/paper/2605.19202/) (2026)
- [Vision-Guided Outdoor Flight and Obstacle Evasion via Reinforcement Learning](https://mlsanity.com/paper/2605.24449/) (2026)

## Abstract (arXiv)

Many existing obstacle avoidance algorithms overlook the crucial balance between safety and agility, especially in environments of varying complexity. In our study, we introduce an obstacle avoidance pipeline based on reinforcement learning. This pipeline enables drones to adapt their flying speed according to the environmental complexity. Moreover, to improve the obstacle avoidance performance in cluttered environments, we propose a novel latent space. The latent space in this representation is explicitly trained to retain memory of previous depth map observations. Our findings confirm that varying speed leads to a superior balance of success rate and agility in cluttered environments. Additionally, our memory-augmented latent representation outperforms the latent representation commonly used in reinforcement learning. Finally, after minimal fine-tuning, we successfully deployed our network on a real drone for enhanced obstacle avoidance.