  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.
