mlsanityData to 14 Oct 2026

Object Manipulation in Marine Environments using Reinforcement Learning

arXiv 2406.03223 ↗First posted 5 Jun 2024cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Performing intervention tasks in the maritime domain is crucial for safety and operational efficiency. The unpredictable and dynamic marine environment makes the intervention tasks such as object manipulation extremely challenging. This study proposes a robust solution for object manipulation from a dock in the presence of disturbances caused by sea waves. To tackle this challenging problem, we apply a deep reinforcement learning (DRL) based algorithm called Soft. Actor-Critic (SAC). SAC employs an actor-critic framework; the actors learn a policy that minimizes an objective function while the critic evaluates the learned policy and provides feedback to guide the actor-learning process. We trained the agent using the PyBullet dynamic simulator and tested it in a realistic simulation environment called MBZIRC maritime simulator. This simulator allows the simulation of different wave conditions according to the World Meteorological Organization (WMO) sea state code. Simulation results demonstrate a high success rate in retrieving the objects from the dock. The trained agent achieved an 80 percent success rate when applied in the simulation environment in the presence of waves characterized by sea state 2, according to the WMO sea state code

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 38 references that are in the map. First posted on arXiv 5 Jun 2024.

Builds on

10 of 38 references.

References · 10

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 743
  3. 2022Robot Operating System 2: Design, Architecture, and Uses In The Wildcited by 199
  4. 2020Learning to Play Table Tennis From Scratch using Muscular Robotscited by 35
  5. 2019Dynamic Cloth Manipulation with Deep Reinforcement Learningcited by 29
  6. 2023Vision-Based Autonomous Navigation for Unmanned Surface Vessel in Extreme Marine Conditionscited by 9
  7. 2022GraspARL: Dynamic Grasping via Adversarial Reinforcement Learningcited by 8
  8. 2017Deep Reinforcement Learning for Robotic Manipulation-The state of the artcited by 7
  9. 2020A reinforcement learning control approach for underwater manipulation under position and torque constraintscited by 2
  10. 2023Dynamic Grasping with a Learned Meta-Controllercited by 2

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  1. 2026Sim-to-reality adaptation for Deep Reinforcement Learning applied to an underwater docking application
  2. 2026SMaRT-Tug: Structured Multi-Agent Reinforcement Learning for Physics-Based Tugboat-Barge Collaborative Manipulation ↗
  3. 2024Non-Prehensile Aerial Manipulation using Model-Based Deep Reinforcement Learning
  4. 2024Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shipping
  5. 2024Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
  6. 2024Variational Autoencoders for exteroceptive perception in reinforcement learning-based collision avoidance
  7. 2026Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning
  8. 2026Robust Autonomous UAV Landing on Maritime Platforms via Multimodal Agentic AI and Active Wave Compensation