# Learning to Assist Drone Landings

arXiv: https://arxiv.org/abs/2011.13146 · HTML: https://mlsanity.com/paper/2011.13146/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [On Optimizing Interventions in Shared Autonomy](https://mlsanity.com/paper/2112.09169/) (2021)
- [Reinforcement Learning-Based Monocular Vision Approach for Autonomous UAV Landing](https://mlsanity.com/paper/2505.06963/) (2025)
- [Self-Supervised Learning to Fly using Efficient Semantic Segmentation and Metric Depth Estimation for Low-Cost Autonomous UAVs](https://mlsanity.com/paper/2510.16624/) (2025)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) are often used for navigating dangerous terrains, however they are difficult to pilot. Due to complex input-output mapping schemes, limited perception, the complex system dynamics and the need to maintain a safe operation distance, novice pilots experience difficulties in performing safe landings in obstacle filled environments. In this work we propose a shared autonomy approach that assists novice pilots to perform safe landings on one of several elevated platforms at a proficiency equal to or greater than experienced pilots. Our approach consists of two modules, a perceptual module and a policy module. The perceptual module compresses high dimensionality RGB-D images into a latent vector trained with a cross-modal variational auto-encoder. The policy module provides assistive control inputs trained with the reinforcement algorithm TD3. We conduct a user study (n=33) where participants land a simulated drone with and without the use of the assistant. Despite the goal platform not being known to the assistant, participants of all skill levels were able to outperform experienced participants while assisted in the task.