Interpretable and Data-Efficient Learning
for Autonomous Systems
ABSTRACT: Artificial intelligence boosted with data-driven methods has surpassed human-level performance in various tasks. However, its application to autonomous systems still faces fundamental challenges such as lack of interpretability and intensive need for data. To address these challenges, this talk presents interpretable and data-efficient learning approaches that weave together theories and techniques in machine learning, formal methods and control theory. Different from traditional learning approaches, our approaches take into account the limited availability of simulated and real data, the uncertainties of the underlying model, and the expressivity and interpretability of high-level knowledge representations.
The first part of this talk focuses on learning high-level knowledge from data and its application in data-driven control. I present some state-of-the-art methods for learning informative temporal logic formulas from data. I further present a data-driven control method of robots in unknown environments that combines the learning of temporal logic formulas and the controller synthesis with the learned temporal logic formulas. The second part of this talk focuses on improving reinforcement learning with high-level knowledge. I present a framework that enables a reinforcement learning agent to reason over its exploration process and distill high-level knowledge (in the form of finite state machines with reward outputs) for effectively guiding its future explorations. The last part of this talk focuses on transfer learning between temporal tasks (i.e., tasks in which the timing of events matters) with logical similarities. I concretize the similarity between temporal tasks through a notion of logical transferability. I then present a transfer learning approach between logically transferable temporal tasks, which can empirically improve the sampling efficiency of the target task by up to two orders of magnitude.
BIO: Zhe Xu is a postdoctoral fellow at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. He received a Ph.D. degree in electrical engineering at Rensselaer Polytechnic Institute in 2018. He received the Howard Kaufman '62 Memorial Fellowship Award in 2016. His research interests lie in the area of formal methods, control theory and machine learning. He has developed various learning, control and verification methods with applications to robotics, power systems, smart buildings and biological systems.