To achieve social interactions within Human-Robot Interaction (HRI) environments is a very challenging task. Most of the current research focuses on Wizard-of-Oz approaches, which neglect the recent development of intelligent robots. On the other hand, real-world scenarios usually do not provide the necessary control and reproducibility which are needed for learning algorithms. In this paper, we propose a virtual simulation environment that implements the Chef's Hat card game, designed to be used in HRI scenarios, to provide a controllable and reproducible scenario for reinforcement-learning algorithms.
@article{arxiv.2003.05861,
title = {The Chef's Hat Simulation Environment for Reinforcement-Learning-Based Agents},
author = {Pablo Barros and Anne C. Bloem and Inge M. Hootsmans and Lena M. Opheij and Romain H. A. Toebosch and Emilia Barakova and Alessandra Sciutti},
journal= {arXiv preprint arXiv:2003.05861},
year = {2020}
}