Papers
35
Total Citations
781
H-Index
14
About
Chuchu Fan is a leading researcher at the intersection of robotics, control theory, and machine learning, with a focus on developing provably safe and scalable autonomy. Her work addresses a fundamental challenge: how to guarantee safety and stability in learning-enabled robotic systems, from single robots to large-scale multi-agent teams. She is best known for pioneering the use of neural Lyapunov and barrier functions to synthesize certified controllers, a breakthrough that bridges the gap between deep learning’s flexibility and formal verification. This work, including her highly cited 2023 survey on safe control with learned certificates (212 citations), has become a cornerstone of the field. Fan has also made major contributions to multi-robot planning, introducing frameworks like GCBF+ for distributed safe control and leveraging large language models for task and motion planning (AutoTAMP, 71 citations). Her research consistently achieves high impact, with multiple papers exceeding 50 citations. Notably, she has advanced vision-based safety using neural radiance fields and developed robust, computationally efficient methods like Shield-MPPI. A recipient of prestigious awards including an NSF CAREER award and an ONR Young Investigator award, Fan is shaping the future of trustworthy, autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2Multi-Agent Motion Planning From Signal Temporal Logic Specifications94 citations · 2022
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- 7Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions27 citations · 2021
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