Professor Calin Belta(University of Maryland): Formal Methods for Safety-Critical Control using Control Barrier Functions

Professor Calin Belta

Professor, Departments of Electrical Engineering and Computer Engineering, University of Maryland

Time: 12:30 pm – 1:30 pm

Date: Friday, February 21th

Location: BC 220

Abstract: In control theory, complicated dynamics such as systems of (nonlinear) differential equations are mostly controlled to achieve stability and to optimize a cost. In formal synthesis, simple systems such as finite state transition graphs modeling computer programs or digital circuits are controlled from specifications such as safety, liveness, or richer requirements expressed as formulas of temporal logic. With the development and integration of cyber-physical and safety-critical systems, there is an increasing need for computational tools for controlling complex systems from rich, temporal logic specifications, while ensuring safety. Recent works proposed computational efficient approaches for safety-critical control using Control Barrier Functions (CBF) and Control Lyapunov Functions (CLF). In this talk, I will show how these approaches can be extended to accommodate systems with high relative degrees, (partially) unknown dynamics, and temporal logic specifications, and to improve the feasibility of the associated optimization problems.

Bio: Calin Belta is the Brendan Iribe Endowed  Professor of  Electrical and Computer Engineering and Computer Science at the University of Maryland, College Park, which is also part of the Maryland Robotics Center (MRC) and the Institute for Systems Research (ISR).  His research focuses on making control and machine learning systems safe and Interpretable,  with particular emphasis on robotics and systems biology. Notable awards include the 2008 AFOSR YIP and the 2005 NSF CAREER. He is a Fellow of the IEEE.

Abhinav Verma(Penn State University): Safe and Performant Policies via Specification Guided Reinforcement Learning

Dr. Abhinav Verma

Assistant Professor, Departments of Electrical Engineering and Computer Science, Penn State University

Time: 12:30 pm – 1:30 pm

Date: Friday, January 31st

Location: BC 220

Abstract: Specifications in linear temporal logic (LTL) offer a simplified way of specifying tasks for policy optimization that may otherwise be difficult to describe with scalar reward functions. However, the standard Reinforcement Learning (RL) frameworks can be too myopic to find maximally satisfying policies. In this talk we will discuss eventual discounting, a value-function based proxy under which one can find policies that satisfy a specification with the highest achievable probability. To improve the efficiency of learning from specifications we combine eventual discounting with LTL-guided Counterfactual Experience Replay, a method for generating off-policy data from on-policy rollouts via counterfactual reasoning. Finally, we will discuss a mechanism for exploiting the compositionality of a LTL specification to provide formal guarantees on the behavior of learnt policies for reach-avoid tasks.

Bio: Dr. Verma Is an Assistant Professor in the Department of Computer Science and Engineering at The Pennsylvania State University. Previously, He was a postdoc at the Institute of Science and Technology (IST) Austria in the Henzinger Group. Before joining IST, He completed his PhD from the University of Texas at Austin advised by Prof. Swarat Chaudhuri. His research lies at the intersection of machine learning and formal methods, with a focus on building intelligent systems that are reliable, transparent, and secure. This work builds connections between the symbolic reasoning and inductive learning paradigms of artificial intelligence.