Learning to Dock: A Simulation-based Study on Closing the Sim2Real Gap in Autonomous Underwater Docking

TL;DR

This paper investigates methods to reduce the sim2real gap in autonomous underwater vehicle docking by evaluating controllers trained in simulation under realistic disturbances, focusing on robustness and transferability.

Contribution

It systematically compares various robustness techniques like randomization and history-conditioned controllers for underwater docking tasks.

Findings

Randomization improves transferability of controllers.

History-conditioned controllers enhance robustness under disturbances.

Insights into mitigating the sim2real gap for marine robotics.

Abstract

Autonomous Underwater Vehicle (AUV) docking in dynamic and uncertain environments is a critical challenge for underwater robotics. Reinforcement learning is a promising method for developing robust controllers, but the disparity between training simulations and the real world, or the sim2real gap, often leads to a significant deterioration in performance. In this work, we perform a simulation study on reducing the sim2real gap in autonomous docking through training various controllers and then evaluating them under realistic disturbances. In particular, we focus on the real-world challenge of docking under different payloads that are potentially outside the original training distribution. We explore existing methods for improving robustness including randomization techniques and history-conditioned controllers. Our findings provide insights into mitigating the sim2real gap when training docking controllers. Furthermore, our work indicates areas of future research that may be beneficial to the marine robotics community.