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

TL;DR

This paper benchmarks various input configurations for Deep Reinforcement Learning agents in quadrotor control, analyzing their robustness and zero-shot sim-to-real transfer capabilities to guide future aerial robot research.

Contribution

It provides a comprehensive benchmark analysis of input data choices for DRL agents in quadrotor control, highlighting their impact on transferability and robustness.

Findings

Certain input configurations improve sim-to-real transfer

Optimized DRL agents demonstrate robust flight control

Input data selection significantly affects transfer success

Abstract

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.