Reinforcement-learning robotic sailboats: simulator and preliminary results
<|reference_start|>Reinforcement-learning robotic sailboats: simulator and preliminary results: This work focuses on the main challenges and problems in developing a virtual oceanic environment reproducing real experiments using Unmanned Surface Vehicles (USV) digital twins. We introduce the key features for building virtual worlds, considering using Reinforcement Learning (RL) agents for autonomous navigation and control. With this in mind, the main problems concern the definition of the simulation equations (physics and mathematics), their effective implementation, and how to include strategies for simulated control and perception (sensors) to be used with RL. We present the modeling, implementation steps, and challenges required to create a functional digital twin based on a real robotic sailing vessel. The application is immediate for developing navigation algorithms based on RL to be applied on real boats.<|reference_end|>
@article{vasconcellos2024reinforcement-learning,
    title={Reinforcement-learning robotic sailboats: simulator and preliminary
  results},
    author={Eduardo Charles Vasconcellos (UFF), Ronald M Sampaio, Andr'e P D
  Ara'ujo (UFF), Esteban Walter Gonzales Clua, Philippe Preux (SEQUEL, GRAppA
  - LIFL), Raphael Guerra, Luiz M G Gonc{c}alves (UFRN), Luis Mart'i, Hernan
  Lira, Nayat Sanchez-Pi},
    journal={NeurIPS 2023 Workshop on Robot Learning Workshop: Pretraining,
  Fine-Tuning, and Generalization with Large Scale Models, Dec 2023, New
  Orelans, United States},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03337},
    primaryClass={cs.RO cs.AI cs.LG}
}