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Yutong Liang

I study dexterous robotic manipulation through human demonstrations, reinforcement learning, and world models.

San Diego · US

Robotics · Character Animation

About

Hi there! I'm Yutong Liang, a first-year graduate student in Computer Science and Engineering at the University of California San Diego, advised by Prof. Xiaolong Wang.

My research aims to develop general-purpose physics agents that can comprehend the world and operate with human-level dexterity.

I believe the next leap in robot manipulation will come from world models that learn how the physical world evolves, human demonstrations that provide scalable motion priors, and reinforcement learning that closes the final gap to dynamic, contact-rich control.

More About Me

Publications

XL-VLA: Cross-Hand Latent Representation for Vision-Language-Action Models

*, Yutong Liang*, , , , , , †,

Takeaway:Embodiment-invariant latent action space enhances performance as demonstrations scale across different hand embodiments, similarly to scaling with additional data from a single hand.

DexterCap: An Affordable and Automated System for Capturing Dexterous Hand-Object Manipulation

Yutong Liang*, *, Yulong Zhang*, Bowen Zhan, ,

Takeaway:Dexterous in-hand manipulation can be captured by providing dense motion information while minimizing interference caused by markers.

GSWorld: Closed-Loop Photo-Realistic Simulation Suite for Robotic Manipulation

*, *, , Yutong Liang, , , , ,

ROBOVERSE: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning

ROBOVERSE: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning

RoboVerse Team

SimiSketch: A Sketching Algorithm for Similarity Estimation

, Yang He*, Yutong Liang*, , , , and

PAPERCode

Education

Recent Writing