Papers
16
Total Citations
637
H-Index
10
About
Sicun Gao is a researcher whose work bridges formal methods, control theory, and machine learning, with a particular focus on safe and stable control systems for robotics. His early contributions established rigorous mathematical foundations for analyzing complex hybrid systems; his δ-reachability framework, introduced through papers like "dReach: δ-Reachability Analysis for Hybrid Systems" (2015, 205 citations) and "Delta-Complete Analysis for Bounded Reachability of Hybrid Systems" (2014), provided powerful tools for verifying nonlinear and hybrid dynamical systems under numerical uncertainty. More recently, Gao has become a leading voice in learning-enabled safe control, co-authoring the widely cited survey "Safe Control with Learned Certificates" (2023, 212 citations), which synthesizes advances in neural Lyapunov, barrier, and contraction methods. His research develops neural controllers with formal guarantees of safety and stability, addressing critical limitations of purely data-driven approaches in robotics. Additional contributions span sim-to-real transfer in reinforcement learning, graph neural network-accelerated motion planning, and scalable dynamic obstacle avoidance. Across these areas, Gao's work stands out for its commitment to making learned control systems both empirically powerful and theoretically trustworthy — an increasingly vital challenge as autonomous systems enter real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1
- 2dReach: δ-Reachability Analysis for Hybrid Systems205 citations · 2015
- 3Stabilizing Neural Control Using Self-Learned Almost Lyapunov Critics34 citations · 2021
- 4Delta-Complete Analysis for Bounded Reachability of Hybrid Systems34 citations · 2014
- 5Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions27 citations · 2021
- 6
- 7
- 8
- 9Interpolants in Nonlinear Theories Over the Reals17 citations · 2016
- 10Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance13 citations · 2023