mlsanityData to 14 Oct 2026

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

arXiv 2506.17823 ↗First posted 21 Jun 2025cs.RO cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 21 Jun 2025.

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15 of 22 references.

References · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  3. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  4. 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 319
  5. 2023Orbit: A Unified Simulation Framework for Interactive Robot Learning Environmentscited by 295
  6. 2020Learning Agile Robotic Locomotion Skills by Imitating Animalscited by 251
  7. 2021Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learningcited by 92
  8. 2019Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotorscited by 45
  9. 2023Sim-to-Real Learning for Humanoid Box Loco-Manipulationcited by 36
  10. 2024Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flightcited by 31
  11. 2020Learning Memory-Based Control for Human-Scale Bipedal Locomotioncited by 28
  12. 2025Aerial Gym Simulator: A Framework for Highly Parallelized Simulation of Aerial Robotscited by 15
3 more
  1. 2023CUREE: A Curious Underwater Robot for Ecosystem Explorationcited by 13
  2. 2021Deep Reinforcement Learning for Continuous Docking Control of Autonomous Underwater Vehicles: A Benchmarking Studycited by 6
  3. 2024Learning to Swim: Reinforcement Learning for 6-DOF Control of Thruster-driven Autonomous Underwater Vehiclescited by 6

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  1. 2026Sim-to-reality adaptation for Deep Reinforcement Learning applied to an underwater docking application
  2. 2025Ocean Diviner: A Diffusion-Augmented Reinforcement Learning Framework for AUV Robust Control in Underwater Tasks
  3. 2026CORL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehicles
  4. 2025Sim2Swim: Zero-Shot Velocity Control for Agile AUV Maneuvering in 3 Minutes
  5. 2024Learning Autonomous Docking Operation of Fully Actuated Autonomous Surface Vessel from Expert data
  6. 2025Fast Policy Learning for 6-DOF Position Control of Underwater Vehicles
  7. 2025Cross-platform Learning-based Fault Tolerant Surfacing Controller for Underwater Robots
  8. 2025When Motion Learns to Listen: Diffusion-Prior Lyapunov Actor-Critic Framework with LLM Guidance for Stable and Robust AUV Control in Underwater Tasks