L4DC 2022: Safety-Aware Preference-Based Learning for Safety-Critical Control

Ryan K. Cosner*, Maegan Tucker, Andrew J. Taylor, Kejun (Amy) Li, Tamas G. Molnar, Wyatt Ubellacker, Anil Alan, Gabor Orosz, Yisong Yue, Aaron D. Ames. [pdf]

Abstract: Bringing dynamic robots into the wild requires a tenuous balance between performance and safety. Yet controllers designed to provide robust safety guarantees often result in conservative behavior, and tuning these controllers to find the ideal trade-off between performance and safety typically requires domain expertise or a carefully constructed reward function. This work presents a design paradigm for systematically achieving behaviors that balance performance and robust safety by integrating safety-aware Preference-Based Learning (PBL) with Control Barrier Functions (CBFs). Fusing these concepts – safety-aware learning and safety-critical control – gives a robust means to achieve safe behaviors on complex robotic systems in practice. We demonstrate the capability of this design paradigm to achieve safe and performant perception-based autonomous operation of a quadrupedal robot both in simulation and experimentally on hardware.