• • Paper: Constrained Exploration in Reinforcement Learning with Optimality Preservation.  https://arxiv.org/abs/2304.03104  

We engage in the research and development of methods and their applications in the areas of Robotics and Autonomous Learning Systems. In Robotics, our research focuses on mechanical manipulation of physical and biological systems at micro to macro scale, with the aim of producing practical and innovative engineering solutions. In Autonomous Learning Systems, we develop autonomous systems that can adapt to uncertain and changing environments while preserving formal guarantees of safety, correctness, and task accomplishment. The foundation of this research is supervisory control theory and discrete-event systems.