Shengfan Cao

I am a PhD researcher at UC Berkeley specializing in safe learning, control theory, and end-to-end autonomy for robotic systems. My work bridges the gap between theoretical guarantees and real-world deployment, focusing on how large-scale data and physical constraints shape learning-based autonomous systems.

My current research highlights include:

• Trusted Autonomy: developing rapidly deployable ground robots that can explore and map unseen terrain with minimal setup.
• Safe Learning: designing constraint-aware imitation learning frameworks that ensure safety even at dynamic handling limits.
• Policy Optimization: formulating sampling-based weight-space projection methods to enforce safety constraints during policy updates.

I leverage tools like PyTorch, ROS 2, and convex optimization to build interpretable and reliable autonomy stacks. I am actively transitioning into industry to apply these principles to large-scale, safety-critical applications.

Please reach out to collaborate! 🚗