Xialin He (何夏麟)

I'm a 1st year Ph.D. student in Computer Science at University of Illinois Urbana-Champaign supervised by Prof. Saurabh Gupta.

Before that, I earned my bachelor degree at ACM Honors Class, Shanghai Jiao Tong University and I was privileged to delve into the application of Reinforcement Learning in Quadruped Robot's locomotion while collaborating with the SJTU APEX lab under the guidance of Prof. Weinan Zhang.

During my senior year, I am fortunate to work with Prof. Xiaolong Wang at UCSD Wang Lab as a research intern. During my time there, I also have the opportunity to collaborate closely with Prof. David Held.

My research interests lie in Reinforcement Learning, Robot Learning, Computer Vision and Control theory.

If you are interested in my work, feel free to to contact me for further discussions or potential collaborations.

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Lead Author Publications

Learning Smooth Humanoid Locomotion through Lipschitz-Constrained Policies

We introduce Lipschitz-Constrained Policies (LCP), a method for achieving smooth locomotion in legged robots by enforcing a Lipschitz constraint on the policy. LCP eliminates the need for non-differentiable smoothing techniques, offering a simpler and robust solution applicable across diverse humanoid robots.

OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning

We present OmniH2O (Omni Human-to-Humanoid), a learning-based system for whole-body humanoid teleoperation and autonomy. Using kinematic pose as a universal control interface, OmniH2O enables various ways for a human to control a full-sized humanoid with dexterous hands and also enables full autonomy by learning from teleoperated demonstrations or integrating with frontier models such as GPT-4o

Visual Manipulation with Legs

We propose a system that enables quadruped to manipulate objects with legs. We use reinforcement learning to train a policy to interact with the object based on point cloud observations, which demonstrates advanced manipulation skills with legs that has not been shown in previous work.

Other Publications

Generalizable Humanoid Manipulation with Improved 3D Diffusion Policies

We developed the Improved 3D Diffusion Policy (iDP3), a 3D visuomotor policy that enables humanoid robots to perform autonomous tasks in varied real-world settings. Unlike traditional models, iDP3 operates without the need for camera calibration or point-cloud segmentation by utilizing egocentric 3D visual representations. This approach allows for effective performance using only lab-collected data.

sim-to-real transfer for quadrupedal locomotion via terrain transformer

we propose Terrain Transformer (TERT), a simple yet effective method to leverage Transformer for quadrupedal locomotion over multiple terrains, including a two-stage training framework to incorporate Transformer with privileged learning.

Education

University of Illinois Urbana-Champaign

Illinois, USA

Shanghai Jiao Tong University

Shanghai, China

Design and source code from Leonid Keselman