Ivan Ovinnikov, Ami Beuret, Flavia Cavaliere, Joachim M. Buhmann
This repository contains RL environments and corresponding config files used to train agents as stated in the paper.
If you find this repository is useful in your research, please cite the paper:
@Article{Beuret2024,
author={Beuret, Ami
and Ovinnikov, Ivan
and Cavaliere, Flavia
and Buhmann, Joachim M.},
title={Fundamentals of Arthroscopic Surgery Training and beyond: a reinforcement learning exploration and benchmark},
journal={International Journal of Computer Assisted Radiology and Surgery},
year={2024},
month={Apr},
day={29},
abstract={This work presents FASTRL, a benchmark set of instrument manipulation tasks adapted to the domain of reinforcement learning and used in simulated surgical training. This benchmark enables and supports the design and training of human-centric reinforcement learning agents which assist and evaluate human trainees in surgical practice.},
issn={1861-6429},
doi={10.1007/s11548-024-03116-z},
url={https://doi.org/10.1007/s11548-024-03116-z}
}