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arXiv · 2407.08263

A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation

Luis F W Batista·Junghwan Ro·Antoine Richard·Pete Schroepfer·Seth Hutchinson·Cedric Pradalier
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Abstract

Despite the increasing adoption of Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), there still remain challenges limiting real-world deployment. In this paper, we first integrate buoyancy and hydrodynamics models into a modern Reinforcement Learning framework to reduce training time. Next, we show how system identification coupled with domain randomization improves the RL agent performance and narrows the sim-to-real gap. Real-world experiments for the task of capturing floating waste show that our approach lowers energy consumption by 13.1\% while reducing task completion time by 7.4\%. These findings, supported by sharing our open-source implementation, hold the potential to impact the efficiency and versatility of ASVs, contributing to environmental conservation efforts.

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Luis F W Batista, Junghwan Ro, Antoine Richard, Pete Schroepfer, Seth Hutchinson, Cedric Pradalier. 2024-07-11. A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation. https://arxiv.org/abs/2407.08263

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