# The Power of Input: Benchmarking Zero-Shot Sim-To-Real Transfer of Reinforcement Learning Control Policies for Quadrotor Control

arXiv: https://arxiv.org/abs/2410.07686 · HTML: https://mlsanity.com/paper/2410.07686/

## Key facts

- Cited by 5 papers in the map (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 13 of its 32 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## 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, 13 of 13)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics](https://mlsanity.com/paper/2005.04269/) (2020)
- [Control of a Quadrotor with Reinforcement Learning](https://mlsanity.com/paper/1707.05110/) (2017)
- [A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight](https://mlsanity.com/paper/2202.10796/) (2022)
- [Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms](https://mlsanity.com/paper/2203.07747/) (2022)
- [PAMPC: Perception-Aware Model Predictive Control for Quadrotors](https://mlsanity.com/paper/1804.04811/) (2018)
- [Learning Minimum-Time Flight in Cluttered Environments](https://mlsanity.com/paper/2203.15052/) (2022)
- [Aggressive Quadrotor Flight Using Curiosity-Driven Reinforcement Learning](https://mlsanity.com/paper/2203.14033/) (2022)
- [Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agents](https://mlsanity.com/paper/2201.02569/) (2022)
- [A Comprehensive Survey of Control Strategies for Autonomous Quadrotors](https://mlsanity.com/paper/2005.09858/) (2020)
- [Learning Model Predictive Control for Quadrotors](https://mlsanity.com/paper/2202.07716/) (2022)
- [Exploring Deep Reinforcement Learning for Robust Target Tracking using Micro Aerial Vehicles](https://mlsanity.com/paper/2312.17552/) (2023)

## Built on by (most cited first, 5 of 5)

- [What Matters in Learning A Zero-Shot Sim-to-Real RL Policy for Quadrotor Control? A Comprehensive Study](https://mlsanity.com/paper/2412.11764/) (2024)
- [Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking](https://mlsanity.com/paper/2506.17832/) (2025)
- [Curriculum-based Sample Efficient Reinforcement Learning for Robust Stabilization of a Quadrotor](https://mlsanity.com/paper/2501.18490/) (2025)
- [RL-based Control of UAS Subject to Significant Disturbance](https://mlsanity.com/paper/2504.08114/) (2025)
- [Online Reinforcement Learning for Safe Gain Scheduling in Nonlinear Quadrotor Control](https://mlsanity.com/paper/2604.16819/) (2026)

## Abstract (arXiv)

In the last decade, data-driven approaches have become popular choices for quadrotor control, thanks to their ability to facilitate the adaptation to unknown or uncertain flight conditions. Among the different data-driven paradigms, Deep Reinforcement Learning (DRL) is currently one of the most explored. However, the design of DRL agents for Micro Aerial Vehicles (MAVs) remains an open challenge. While some works have studied the output configuration of these agents (i.e., what kind of control to compute), there is no general consensus on the type of input data these approaches should employ. Multiple works simply provide the DRL agent with full state information, without questioning if this might be redundant and unnecessarily complicate the learning process, or pose superfluous constraints on the availability of such information in real platforms. In this work, we provide an in-depth benchmark analysis of different configurations of the observation space. We optimize multiple DRL agents in simulated environments with different input choices and study their robustness and their sim-to-real transfer capabilities with zero-shot adaptation. We believe that the outcomes and discussions presented in this work supported by extensive experimental results could be an important milestone in guiding future research on the development of DRL agents for aerial robot tasks.