# A Learning-based Quadcopter Controller with Extreme Adaptation

arXiv: https://arxiv.org/abs/2409.12949 · HTML: https://mlsanity.com/paper/2409.12949/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2024.

## Built on by (most cited first, 17 of 17)

- [SOUS VIDE: Cooking Visual Drone Navigation Policies in a Gaussian Splatting Vacuum](https://mlsanity.com/paper/2412.16346/) (2024)
- [RAPTOR: A Foundation Policy for Quadrotor Control](https://mlsanity.com/paper/2509.11481/) (2025)
- [Agile Flight Emerges from Multi-Agent Competitive Racing](https://mlsanity.com/paper/2512.11781/) (2025)
- [Reinforcement Learning-based Fault-Tolerant Control for Quadrotor with Online Transformer Adaptation](https://mlsanity.com/paper/2505.08223/) (2025)
- [Curriculum-based Sample Efficient Reinforcement Learning for Robust Stabilization of a Quadrotor](https://mlsanity.com/paper/2501.18490/) (2025)
- [Efficient Knowledge Transfer for Jump-Starting Control Policy Learning of Multirotors through Physics-Aware Neural Architectures](https://mlsanity.com/paper/2602.15533/) (2026)
- [Rapid Embodiment Adaptation for Quadrupedal Locomotion](https://mlsanity.com/paper/2608.01506/) (2026)
- [Multitask Reinforcement Learning for Quadcopter Attitude Stabilization and Tracking using Graph Policy](https://mlsanity.com/paper/2503.08259/) (2025)
- [Dynamics-Invariant Quadrotor Control using Scale-Aware Deep Reinforcement Learning](https://mlsanity.com/paper/2503.09622/) (2025)
- [Rich State Observations Empower Reinforcement Learning to Surpass PID: A Drone Ball Balancing Study](https://mlsanity.com/paper/2509.21122/) (2025)
- [AD-NODE: Adaptive Dynamics Learning with Neural ODEs for Mobile Robots Control](https://mlsanity.com/paper/2510.05443/) (2025)
- [MAVEN: A Meta-Reinforcement Learning Framework for Varying-Dynamics Expertise in Agile Quadrotor Maneuvers](https://mlsanity.com/paper/2603.10714/) (2026)
- [Safety Certification is Classification](https://mlsanity.com/paper/2605.06087/) (2026)
- [Adaptive Outer-Loop Control of Quadrotors via Reinforcement Learning](https://mlsanity.com/paper/2605.16015/) (2026)
- [Vision-Guided Outdoor Flight and Obstacle Evasion via Reinforcement Learning](https://mlsanity.com/paper/2605.24449/) (2026)
- [World-Task Factorization for Robot Learning](https://mlsanity.com/paper/2606.02027/) (2026)
- [Embodiment-conditioned Generalist Control for Multirotor Aerial Robots](https://mlsanity.com/paper/2606.10857/) (2026)

## Abstract (arXiv)

This paper introduces a learning-based low-level controller for quadcopters, which adaptively controls quadcopters with significant variations in mass, size, and actuator capabilities. Our approach leverages a combination of imitation learning and reinforcement learning, creating a fast-adapting and general control framework for quadcopters that eliminates the need for precise model estimation or manual tuning. The controller estimates a latent representation of the vehicle's system parameters from sensor-action history, enabling it to adapt swiftly to diverse dynamics. Extensive evaluations in simulation demonstrate the controller's ability to generalize to unseen quadcopter parameters, with an adaptation range up to 16 times broader than the training set. In real-world tests, the controller is successfully deployed on quadcopters with mass differences of 3.7 times and propeller constants varying by more than 100 times, while also showing rapid adaptation to disturbances such as off-center payloads and motor failures. These results highlight the potential of our controller in extreme adaptation to simplify the design process and enhance the reliability of autonomous drone operations in unpredictable environments. The video and code are at: https://github.com/muellerlab/xadapt_ctrl