Vessel-to-Vessel Motion Compensation with Reinforcement Learning


  • Sverre Herland NTNU
  • Kerstin Bach Norwegian University of Science and Technology



Deep Reinforcement Learning, Marine Operations, Manipulator Arm


Actuation delay poses a challenge for robotic arms and cranes. This is especially the case in dynamic environments where the robot arm or the objects it is trying to manipulate are moved by exogenous forces. In this paper, we consider the task of using a robotic arm to compensate for relative motion between two vessels at sea. We construct a hybrid controller that combines an Inverse Kinematic (IK) solver with a Reinforcement Learning (RL) agent that issues small corrections to the IK input. The solution is empirically evaluated in a simulated environment under several sea states and actuation delays. We observe that more intense waves and larger actuation delays have an adverse effect on the IK controller's ability to compensate for vessel motion. The RL agent is shown to be effective at mitigating large parts of these errors, both in the average case and in the worst case. Its modest requirement for sensory information, combined with the inherent safety in only making small adjustments, also makes it a promising approach for real-world deployment.




How to Cite

Herland, S., & Bach, K. (2023). Vessel-to-Vessel Motion Compensation with Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15682-15688.



IAAI Technical Track on emerging Applications of AI