Wenli Xiao

I am a first-year Master (MS, Robotics) student at CMU Robotics Intitute. I am currently working in LeCAR Lab, co-advised by Prof. Guanya Shi and Prof. John Dolan.

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My ultimate goal is to build deployable robotic systems and facilitate robots working in our daily lives!
My research focuses on using data-driven approaches and combining large-scale (Reinforcement) Learning-based methods with control-theoretical frameworks to tackle: 1) safety, 2) agility, and 3) adaptivity, to make robots more deployable. I am passionate about real robots and have extensive experience with 1/10 Racing Cars, quadrupedal robots, and Humanoids! (*equal contribution)

WoCoCo: Learning Whole-Body Humanoid Control with Sequential Contacts
Chong Zhang*, Wenli Xiao*, Tairan He, Guanya Shi
2024, Under Review
arXiv / website / video / introduction / twitter

TL;DR: WoCoCo is the first unified RL framework to learn whole-body humanoid control with sequential contacts.

OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning
Tairan He*, Zhengyi Luo*, Xialin He*, Wenli Xiao, Chong Zhang, Weinan Zhang, Kris Kitani, Changliu Liu, Guanya Shi
2024, Under Review
arXiv / website / video / Twitter

TL;DR: OmniH2O provides the first universal whole-body humanoid control interface that enables diverse teleoperation and autonomy methods.

Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation
Tairan He*, Zhengyi Luo*. Wenli Xiao, Chong Zhang, Kris Kitani, Changliu Liu, Guanya Shi
2024, Under Review
arXiv / website / video

TL;DR: H2O enables real-time whole-body teleoperation of a full-sized humanoid to perform tasks like pick and place, walking, kicking, boxing, etc.

Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion
Tairan He*, Chong Zhang* Wenli Xiao, Guanqi He, Changliu Liu, Guanya Shi
RSS 2024
arXiv / website / video

Legged robots navigating cluttered environments must be jointly agile for efficient task execution and safe to avoid collisions with obstacles or humans. Existing studies either develop conservative controllers (< 1.0 m/s) to ensure safety, or focus on agility without considering potentially fatal collisions. This paper introduces Agile But Safe (ABS), a learning-based control framework that enables agile and collision-free locomotion for quadrupedal robots.

Safe Deep Policy Adaptation
Wenli Xiao*, Tairan He*, John Dolan, Guanya Shi
ICRA 2024
CoRL 2023 Deployable Workshop

arXiv / website / video

This paper jointly tackles policy adaptation and safe reinforcement learning with safety guarantees. Comprehensive experiments on (1) classic control problems (Inverted Pendulum), (2) simulation benchmarks (Safety Gym), and (3) a real-world agile robotics platform (RC Car) demonstrate great superiority of SafeDPA in both safety and task performance, over state-of-the-art baselines.

Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning
Wenli Xiao, Yiwei Lyu, John Dolan
AAMAS 2023 (oral)

We propose the Model-based Dynamic Shielding (MBDS) framework to address the safety challenges in Multi-Agent Reinforcement Learning (MARL), while providing formal guarantees through the application of Linear Temporal Logic (LTL) in its construction.

Tackling system and statistical heterogeneity for federated learning with adaptive client sampling
Bing Luo, Wenli Xiao, Shiqiang Wang, Jianwei Huang, Leandros Tassiulas

This paper presents an adaptive client sampling algorithm that minimizes wall-clock convergence time in Federated Learning by addressing system and statistical heterogeneity, resulting in up to 73% less time spent compared to baselines (weighted sampling, uniform sampling, .etc).

Carnegie Mellon University, Pittsburgh, PA, USA
MS, Robotics • Sep. 2023 to May. 2025 (expected)
UC Berkeley, Berkeley, CA, USA
Visiting Student • Jan. 2022 to May. 2022
The Chinese University of Hong Kong, Shenzhen, China
B.S. in Electric Information Engineering • Sep. 2019 to Jun. 2023
Robotics Institute Summer Scholar (RISS), Carnegie Mellon University
Research Intern • June. 2022 to Aug. 2023
Advisor: Prof. John Dolan and Yiwei Lyu
RISE Lab, UC Berkeley
Research Intern • March. 2022 to May. 2022
Advisor: Prof. Joseph E. Gonzalez and Tianjun Zhang
NCEL Lab, Shenzhen Institute of Artificial Intelligence and Robotics for Society (Shenzhen AIRS)
Research Intern • Aug. 2020 to March. 2022
Advisor: Prof. Jianwei Huang and Prof. Bing Luo

Updated at June. 2024
Thanks Jon Barron for this amazing template