# Agile Fall Recovery for Quadrotors with Bidirectional Thrust via Reinforcement Learning

arXiv: https://arxiv.org/abs/2606.16513 · HTML: https://mlsanity.com/paper/2606.16513/

## 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 19 of its 30 references that are in the map.
- First posted on arXiv 15 Jun 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, 19 of 19)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2304.13653/) (2023)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Control of a Quadrotor with Reinforcement Learning](https://mlsanity.com/paper/1707.05110/) (2017)
- [Deep Drone Acrobatics](https://mlsanity.com/paper/2006.05768/) (2020)
- [RSL-RL: A Learning Library for Robotics Research](https://mlsanity.com/paper/2509.10771/) (2025)
- [A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight](https://mlsanity.com/paper/2202.10796/) (2022)
- [Learning Humanoid Standing-up Control across Diverse Postures](https://mlsanity.com/paper/2502.08378/) (2025)
- [Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning](https://mlsanity.com/paper/1901.07517/) (2019)
- [Learning to Fly in Seconds](https://mlsanity.com/paper/2311.13081/) (2023)
- [Demonstrating Agile Flight from Pixels without State Estimation](https://mlsanity.com/paper/2406.12505/) (2024)
- [Learning Quadrotor Control From Visual Features Using Differentiable Simulation](https://mlsanity.com/paper/2410.15979/) (2024)
- [Aerial Gym Simulator: A Framework for Highly Parallelized Simulation of Aerial Robots](https://mlsanity.com/paper/2503.01471/) (2025)
- [Whole-Body Control Through Narrow Gaps From Pixels To Action](https://mlsanity.com/paper/2409.00895/) (2024)
- [Flying on Point Clouds with Reinforcement Learning](https://mlsanity.com/paper/2503.00496/) (2025)
- [Ground-Effect-Aware Modeling and Control for Multicopters](https://mlsanity.com/paper/2506.19424/) (2025)
- [Precise Aggressive Aerial Maneuvers with Sensorimotor Policies](https://mlsanity.com/paper/2604.05828/) (2026)
- [Design and Autonomous Stabilization of a Ballistically Launched Multirotor](https://mlsanity.com/paper/1911.10269/) (2019)

## Abstract (arXiv)

Autonomous fall recovery is a critical capability for quadrotors operating in real-world environments, where collisions or failures may leave the vehicle resting on the ground in an arbitrary attitude. This problem is challenging because recovery must be achieved under limited onboard sensing, in constrained free space, with ground contact, and in the presence of unknown disturbances. In this letter, we present an RL-based framework for autonomous fall recovery of a quadrotor from arbitrary ground attitudes to stable hover using only lightweight onboard sensors. To address severe partial observability and intermittent sensor invalidity, we train a recurrent policy within an asymmetric actor--critic architecture, leveraging an Incremental Nonlinear Dynamic Inversion (INDI) controller to track the policy output. Combined with high-fidelity simulations of motor response and optical flow, the overall training framework significantly reduces the sim-to-real gap. Simulation ablation studies validate the importance of the main design choices, while real-world experiments demonstrate zero-shot transfer and robust recovery under different initial attitudes, wind disturbances, and additional payloads. These results demonstrate that agile quadrotor fall recovery can be achieved without explicit state estimation using only limited and unreliable onboard sensing.