# Learning Agile, Vision-based Drone Flight: from Simulation to Reality

arXiv: https://arxiv.org/abs/2304.04128 · HTML: https://mlsanity.com/paper/2304.04128/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2023.

## Built on by (most cited first, 2 of 2)

- [Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigation](https://mlsanity.com/paper/2306.11290/) (2023)
- [End-to-End Imitation Learning for Optimal Asteroid Proximity Operations](https://mlsanity.com/paper/2502.01034/) (2025)

## Abstract (arXiv)

We present our latest research in learning deep sensorimotor policies for agile, vision-based quadrotor flight. We show methodologies for the successful transfer of such policies from simulation to the real world. In addition, we discuss the open research questions that still need to be answered to improve the agility and robustness of autonomous drones toward human-pilot performance.