I am Zixuan Huang, a fourth year PhD student in Robotics at University of Michigan studying learning for robot manipulation. I'm very fortunate to be advised by Prof. Dmitry Berenson.
Prior to UMich, I was a Master student in Robotics at Carnegie Mellon University advised by Prof. David Held.
I obtained a B.S. in Computer Science from the City University of Hong Kong, where I explored privacy-preserving machine learning and non-photorealistic rendering under the supervision of Prof. Antoni B. Chan, Prof. Cong Wang and Prof. Jing Liao
03/2025: I will join the Amazon's Frontier AI & Robotics team at SF as an intern this summer.
02/2025: Our paper "Implicit Contact Diffuser" has been accepted to ICRA 2025!
Research
My current research focuses on the intersection of robotics, computer vision, reinforcement learning and planning. My goal is to enable robot to learn, adapt and operate autonormously in the unstructured envrionments.
We proposed a unified diffusion framework for modeling multimodal trajectories. Once trained, it can serve as a policy, planner, dynamics model, state estimator, and anomaly detector
We enable the robot to reason about the changing contacts between objects and environment learning a diffusion model over neural contact representations.
Learning a particle-based dynamics model on the visible part of the cloth enables efficient planning for cloth smoothing. We show that implicit occlusioning by graph imitation further improves the performance.
