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About

Bike Zhang is a robotics researcher whose work spans safety-critical control theory, legged locomotion, and the application of machine learning to autonomous robotic systems. Zhang has made significant contributions to the intersection of formal safety guarantees and real-world robot deployment, most notably through pioneering work integrating Control Barrier Functions (CBFs) with Model Predictive Control (MPC) — a framework that has attracted over 300 citations and addressed one of robotics' most persistent open challenges: ensuring constraint satisfaction without sacrificing optimal performance. Zhang's research portfolio reflects a natural evolution from theoretical foundations to complex physical systems. Early work on CBF-based safety filters for discrete-time systems laid the groundwork for subsequent contributions in dynamic legged manipulation and multi-robot coordination. More recently, Zhang has emerged as a key contributor to humanoid robotics, co-authoring the widely recognized "Real-world Humanoid Locomotion with Reinforcement Learning" (151 citations) and leading development of the Berkeley Humanoid platform — a low-cost, learning-optimized research robot. Additional work exploring large language models as high-level robot planners and torque-based RL controllers for quadrupeds demonstrates Zhang's breadth across the full autonomy stack, making their research profile particularly valuable for students working at the frontier of embodied AI.