通过仿真研究提升水下机器人自主对接的实测鲁棒性
Learning to Dock: A Simulation-based Study on Closing the Sim2Real Gap in Autonomous Underwater Docking
- 在动态扰动下测试多种强化学习控制器
- 不同负载工况下性能下降显著但可缓解
- 适合关注水下机器人实操落地的研究者
自主水下航行器（AUV）在动态不确定环境中实现对接是水下机器人领域的重要挑战。强化学习虽具潜力，但训练环境与真实世界的差异（即模拟到现实的差距）常导致性能大幅下降。本文通过仿真研究，评估多种控制器在真实扰动下的表现，重点关注训练分布外的不同载荷条件对对接性能的影响。探索了随机化技术与历史依赖型控制器等增强鲁棒性的方法。结果为缩小对接控制中的模拟到现实差距提供了实践见解，并指出了未来对海洋机器人领域有帮助的研究方向。
原文摘要 · Abstract (English)
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.
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