The Chef's Hat Simulation Environment for Reinforcement-Learning-Based Agents
This work addresses the problem of lacking controlled and reproducible environments for learning algorithms in HRI, though it appears incremental as it builds on existing simulation approaches.
The paper tackles the challenge of achieving social interactions in Human-Robot Interaction (HRI) by proposing a virtual simulation environment for the Chef's Hat card game, designed to provide a controllable and reproducible scenario for reinforcement-learning algorithms.
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.