Learning to Dock: A Simulation-based Study on Closing the Sim2Real Gap in Autonomous Underwater Docking
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912443997880320 |
|---|---|
| author | Chang, Kevin Vivekanandan, Rakesh Pragin, Noah Bullock, Sean Hollinger, Geoffrey |
| author_facet | Chang, Kevin Vivekanandan, Rakesh Pragin, Noah Bullock, Sean Hollinger, Geoffrey |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Dock: A Simulation-based Study on Closing the Sim2Real Gap in Autonomous Underwater Docking Chang, Kevin Vivekanandan, Rakesh Pragin, Noah Bullock, Sean Hollinger, Geoffrey Robotics Artificial Intelligence Machine Learning 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. |
| title | Learning to Dock: A Simulation-based Study on Closing the Sim2Real Gap in Autonomous Underwater Docking |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.17823 |