# Learning Agile Intruder Interception using Differentiable Quadrotor Dynamics

arXiv: https://arxiv.org/abs/2607.02472 · HTML: https://mlsanity.com/paper/2607.02472/

## Key facts

- No citing papers in the map yet (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 16 of its 29 references that are in the map.
- First posted on arXiv 2 Jul 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, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [Back to Newton's Laws: Learning Vision-based Agile Flight via Differentiable Physics](https://mlsanity.com/paper/2407.10648/) (2024)
- [Training Efficient Controllers via Analytic Policy Gradient](https://mlsanity.com/paper/2209.13052/) (2022)
- [Quadrotor Navigation using Reinforcement Learning with Privileged Information](https://mlsanity.com/paper/2509.08177/) (2025)
- [On Onboard LiDAR-based Flying Object Detection](https://mlsanity.com/paper/2303.05404/) (2023)
- [Towards Safe Mid-Air Drone Interception: Strategies for Tracking & Capture](https://mlsanity.com/paper/2405.13542/) (2024)
- [Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games](https://mlsanity.com/paper/2506.02849/) (2025)
- [Simple but Stable, Fast and Safe: Achieve End-to-end Control by High-Fidelity Differentiable Simulation](https://mlsanity.com/paper/2604.10548/) (2026)
- [Precise Interception Flight Targets by Image-based Visual Servoing of Multicopter](https://mlsanity.com/paper/2409.17497/) (2024)
- [Non-Equilibrium MAV-Capture-MAV via Time-Optimal Planning and Reinforcement Learning](https://mlsanity.com/paper/2503.06578/) (2025)
- [Autonomous 3D Moving Target Encirclement and Interception with Range measurement](https://mlsanity.com/paper/2506.13106/) (2025)
- [Agile Interception of a Flying Target using Competitive Reinforcement Learning](https://mlsanity.com/paper/2603.16279/) (2026)

## Abstract (arXiv)

This paper presents a methodology for learning a control policy to intercept an intruder using the 3D direction unit vector to the intruder and the interceptor state. Prior deep reinforcement learning approaches assume either relative position or distance to the intruder is available, but this information is not readily accessible in real-world applications that employ passive, monocular camera sensors. Instead, we propose a solution that leverages an analytical policy gradient method using differentiable quadrotor dynamics to learn agile interception at speeds up to 10 m/s. The proposed approach outperforms baseline methods that utilize simplified point mass dynamics by an average of 30%.