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, motivated by practical challenges.

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 FATE's predicted optical flow along a diagonal; a magnifier on the axe blade shows learned correspondence tokens following its edge.

NeurIPS 2026

Dense Flow from Adaptive Correspondence

Cheng-Lin Hsieh and Stella X. Yu

We estimate how every pixel moves between two images without matching each pixel separately. In FATE, learned pixel groups gather visual context for reliable matching, then the same grouping combines their motion estimates to recover fine motion detail.

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