# Inclined Quadrotor Landing using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2103.09043 · HTML: https://mlsanity.com/paper/2103.09043/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 16 Mar 2021.

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Control of a Quadrotor with Reinforcement Learning](https://mlsanity.com/paper/1707.05110/) (2017)
- [Neural Lander: Stable Drone Landing Control using Learned Dynamics](https://mlsanity.com/paper/1811.08027/) (2018)
- [Reinforcement Learning for UAV Attitude Control](https://mlsanity.com/paper/1804.04154/) (2018)
- [Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors](https://mlsanity.com/paper/1903.04628/) (2019)
- [Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning](https://mlsanity.com/paper/1901.03737/) (2019)

## Built on by (most cited first, 7 of 7)

- [Learning to Fly in Seconds](https://mlsanity.com/paper/2311.13081/) (2023)
- [Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors](https://mlsanity.com/paper/2201.01369/) (2022)
- [Deep Reinforcement Learning for Sim-to-Real Policy Transfer of VTOL-UAVs Offshore Docking Operations](https://mlsanity.com/paper/2406.00887/) (2024)
- [Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter Inference on Multi-Tasking Autonomous Nano-Drones](https://mlsanity.com/paper/2407.02405/) (2024)
- [EAGERx: Graph-Based Framework for Sim2real Robot Learning](https://mlsanity.com/paper/2407.04328/) (2024)
- [PerchRL: Vision-Based Agile Perching on Inclined Platforms under Rapid and Irregular Motion](https://mlsanity.com/paper/2606.03441/) (2026)
- [Barrier-Shaped Recurrent Reinforcement Learning for Autonomous Landing on a Heaving Ship Deck](https://mlsanity.com/paper/2610.03924/) (2026)

## Abstract (arXiv)

Landing a quadrotor on an inclined surface is a challenging maneuver. The final state of any inclined landing trajectory is not an equilibrium, which precludes the use of most conventional control methods. We propose a deep reinforcement learning approach to design an autonomous landing controller for inclined surfaces. Using the proximal policy optimization (PPO) algorithm with sparse rewards and a tailored curriculum learning approach, an inclined landing policy can be trained in simulation in less than 90 minutes on a standard laptop. The policy then directly runs on a real Crazyflie 2.1 quadrotor and successfully performs real inclined landings in a flying arena. A single policy evaluation takes approximately 2.5,ms, which makes it suitable for a future embedded implementation on the quadrotor.