Cheng-Lin (Stanley) Hsieh

Cheng-Lin (Stanley) Hsieh謝承霖

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.

clhsieh@umich.edu

Portrait of Cheng-Lin Hsieh

Seeking summer 2027 research internships in robotics and computer vision.

Selected research

* Equal contribution

A frame of a girl and a dragon turns into DACE's predicted optical flow along a diagonal; a magnifier on the axe blade shows learned correspondence units following its edge.

NeurIPS 2026

Dense Flow from Adaptive Correspondence

Cheng-Lin Hsieh and Stella X. Yu

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.

CoRL 2026 Workshops: Scaling H2R & Contact-Rich Loco-Manipulation


Under review

Test-Time Motion Steering for Perceptive Humanoid Fall Recovery

Osher Azulay*, Cheng-Lin Hsieh*, and Stella X. Yu

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.

Other publications

  1. 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.

  2. EECS: Efficient End-Cloud Collaborative System with On-Demand Offloading Mechanism for Convolutional Neural Network ↗

    Y.-C. Lo, C.-L. Hsieh, H.-W. Chiang, and A.-Y. A. Wu.

    IEEE Internet of Things Journal, 12(8):11081–11095, 2025.

  3. Trainable Policy for the On-Demand Offloading on Edge-Cloud Collaborative System

    C.-L. Hsieh, Y.-C. Lo, and A.-Y. A. Wu.

    IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2023.

  4. Constraints-Aware Trainable Pruning with System Optimization for the On-Demand Offloading Edge-Cloud Collaborative System

    Y.-C. Lo, C.-L. Hsieh, and A.-Y. A. Wu.

    IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2023.

Teaching

Graduate Student Instructor · University of Michigan