Control Barrier Functions: Guaranteeing Safety in Theory and Practice

Aaron D. Ames is the Bren Professor of Mechanical and Civil Engineering and Control and Dynamical Systems at the California Institute of Technology.  He received a B.S. in Mechanical Engineering and a B.A. in Mathematics from the University of St. Thomas in 2001, and received a M.A. in Mathematics and a Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley in 2006.  He served as a Postdoctoral Scholar in Control and Dynamical Systems at Caltech from 2006 to 2008, began his faculty career at Texas A&M University in 2008, and was an Associate Professor in Mechanical Engineering and Electrical & Computer Engineering at the Georgia Institute of Technology before joining Caltech in 2017.  He is an IEEE Fellow, and has received numerous awards for his research in control, including: the 2005 Leon O. Chua Award for achievement in nonlinear science, the 2006 Bernard Friedman Memorial Prize in Applied Mathematics, the NSF CAREER award in 2010, the 2015 Donald P. Eckman Award recognizing an outstanding young engineer in the field of automatic control, and the 2019 Antonio Ruberti Young Researcher Prize awarded for outstanding achievement in systems and control.  Additionally, his work has received multiple best paper awards at top conferences on robotics and control, e.g., the Best Paper Award of ICRA (in 2020 and 2023).  His research interests span the areas of nonlinear control, safety-critical, cyber-physical and hybrid systems, with a special focus on applications to robotic systems—both formally and through experimental validation.  His lab designs, builds and tests novel robotics with the goal of demonstrating theory in practice.  The application of these ideas range from enabling autonomy in robotic systems while ensuring safety, to improving the locomotion capabilities of the mobility impaired.

Keynote Abstract

As robotic systems pervade our everyday lives, the question becomes: how can we trust that robots will operate safely around us?  This question is especially prevalent given the rise of complex algorithms realizing autonomous behavior, including the widespread use of machine learning.  This presentation frames safety in a control-theoretic context, and thereby provides a formal answer to the question of how to ensure safe behavior on robotic system: control barrier functions (CBFs).  These Lyapunov-like functions generate controllers that provably guarantee forward invariance of “safe” sets.  Moreover, CBFs lead to the notion of a safety filter that minimally modifies an existing controller to ensure safety of the system—even if this controller is unknown, the result of a learning-based process, or operating as part of a broader autonomy stack.  The ability to translate these theoretic guarantees to practice will be framed in the context of layered control architectures.  This will be demonstrated through their extensive implementation on a wide variety of highly dynamic robotic systems: from ground robots, to drones, to legged robots, to robotic assistive devices.