I’m a Ph.D. candidate in Computer Science and Engineering at the University of Michigan, advised by Stella X. Yu. I received my B.S. in Electrical Engineering from National Taiwan University. I study robot learning and visual perception in real-world settings.
Robot learning. I am interested in scalable learning from diverse data and experience, with human motion as one example of a useful prior. My current work focuses on contact-rich interaction and humanoid whole-body control.
Visual perception. I study spatial and dynamic scene understanding. I also explore embodied perception through semantic localization and fine-grained video understanding.
We let an optical flow model learn which pixels to match together. In DACE, each group of pixels shares one motion estimate: it gathers evidence from the whole group and returns the motion to each pixel, cutting errors most where no match is visible and near motion boundaries.
We turn getting back up into a behavior the robot can adapt and steer. In RISE, a visual diffusion model lets us guide heading, timing, and route at test time, while a depth-aware controller carries out the recovery on a real Unitree G1.
Where to Hitch? Finding Small, Rare Parts Across Long Spatiotemporal Contexts
C.-L. Hsieh*, Z. Wang*, S. X. Yu, E. Corona, S. Sasidharan, and E. D. Sumer.
NeurIPS 2026 Workshop: LCFM.
We find tiny parts in long videos by choosing what a video model sees. In HitchIt, we zoom from the trailer to its hitch coupler in a few close-up frames, then track the coupler through the whole video, with no task-specific training.