# Learning Minimum-Time Flight in Cluttered Environments

arXiv: https://arxiv.org/abs/2203.15052 · HTML: https://mlsanity.com/paper/2203.15052/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2022.

## Built on by (most cited first, 16 of 16)

- [Learning Perception-Aware Agile Flight in Cluttered Environments](https://mlsanity.com/paper/2210.01841/) (2022)
- [Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning](https://mlsanity.com/paper/2407.00324/) (2024)
- [What Matters in Learning A Zero-Shot Sim-to-Real RL Policy for Quadrotor Control? A Comprehensive Study](https://mlsanity.com/paper/2412.11764/) (2024)
- [One Net to Rule Them All: Domain Randomization in Quadcopter Racing Across Different Platforms](https://mlsanity.com/paper/2504.21586/) (2025)
- [The Power of Input: Benchmarking Zero-Shot Sim-To-Real Transfer of Reinforcement Learning Control Policies for Quadrotor Control](https://mlsanity.com/paper/2410.07686/) (2024)
- [Mastering Diverse, Unknown, and Cluttered Tracks for Robust Vision-Based Drone Racing](https://mlsanity.com/paper/2512.09571/) (2025)
- [Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning](https://mlsanity.com/paper/2406.19646/) (2024)
- [Towards Task-Oriented Flying: Framework, Infrastructure, and Principles](https://mlsanity.com/paper/2504.15129/) (2025)
- [JuggleRL: Mastering Ball Juggling with a Quadrotor via Deep Reinforcement Learning](https://mlsanity.com/paper/2509.24892/) (2025)
- [Efficient Knowledge Transfer for Jump-Starting Control Policy Learning of Multirotors through Physics-Aware Neural Architectures](https://mlsanity.com/paper/2602.15533/) (2026)
- [Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles](https://mlsanity.com/paper/2602.24030/) (2026)
- [Non-Prehensile Aerial Manipulation using Model-Based Deep Reinforcement Learning](https://mlsanity.com/paper/2407.00889/) (2024)
- [RESC: A Reinforcement Learning Based Search-to-Control Framework for Quadrotor Local Planning in Dense Environments](https://mlsanity.com/paper/2408.00275/) (2024)
- [Real-Time Sampling-based Online Planning for Drone Interception](https://mlsanity.com/paper/2502.14231/) (2025)
- [Decision Transformer-Based Drone Trajectory Planning with Dynamic Safety-Efficiency Trade-Offs](https://mlsanity.com/paper/2507.21506/) (2025)
- [Real-DRL: Teach and Learn in Reality](https://mlsanity.com/paper/2511.00112/) (2025)

## Abstract (arXiv)

We tackle the problem of minimum-time flight for a quadrotor through a sequence of waypoints in the presence of obstacles while exploiting the full quadrotor dynamics. Early works relied on simplified dynamics or polynomial trajectory representations that did not exploit the full actuator potential of the quadrotor, and, thus, resulted in suboptimal solutions. Recent works can plan minimum-time trajectories; yet, the trajectories are executed with control methods that do not account for obstacles. Thus, a successful execution of such trajectories is prone to errors due to model mismatch and in-flight disturbances. To this end, we leverage deep reinforcement learning and classical topological path planning to train robust neural-network controllers for minimum-time quadrotor flight in cluttered environments. The resulting neural network controller demonstrates substantially better performance of up to 19% over state-of-the-art methods. More importantly, the learned policy solves the planning and control problem simultaneously online to account for disturbances, thus achieving much higher robustness. As such, the presented method achieves 100% success rate of flying minimum-time policies without collision, while traditional planning and control approaches achieve only 40%. The proposed method is validated in both simulation and the real world, with quadrotor speeds of up to 42km/h and accelerations of 3.6g.