Yuxiang Yang

I'm a 3rd year PhD student at University of Washington, where I work with Prof. Byron Boots at the UW Robot Learning Lab. My research interest lies in the combination of machine learning and optimal control, with an application on quadrupedal robots. I also collaborate with researchers at Robotics at Google on several locomotion projects. Prior to PhD, I obtained my undergraduate degree at UC Berkeley, and spent two years as an AI Resident in Google.

profile photo

Email  /  GitHub  /  Google Scholar  /  LinkedIn  /  CV



I'm generally interested in robotics, control theory and machine learning. I would love to see the combination of them that solves complex, real-world problems.

project image

Learning Semantics-Aware Locomotion Skills from Human Demonstrations

Yuxiang Yang*, Xiangyun Meng, Wenhao Yu, Tingnan Zhang, Jie Tan, Byron Boots
Conference on Robot Learning (CoRL) 2022
arxiv / video / website /

We build a framework for quadrupedal robots to learn offroad locomotion skills based on perceived terrain semantics.

project image

Fast and Efficient Locomotion via Learned Gait Transitions

Yuxiang Yang*, Tingnan Zhang, Erwin Coumans, Jie Tan, Byron Boots
Conference on Robot Learning (CoRL) 2021, Best Systems Paper Award Finalist
arxiv / video / code / website /

We design a hierarchical framework for quadrupedal robots to learn energy-efficient gait patterns for quadrupedal robots.

project image

Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning

Xingyou Song*, Yuxiang Yang*, Krzysztof Choromanski, Ken Caluwaerts, Wenbo Gao, Chelsea Finn, Jie Tan
International Conference on Intelligent Robots and Systems (IROS) 2020
arxiv / video /

We use evolutionary-strategy (ES) based meta learning to perform dynamics adaptation on a real legged robot.

project image

ES-MAML: Simple Hessian-Free Meta Learning

Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang
International Conference on Learning Representations (ICLR) 2020
arxiv /

We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES).

project image

Data Efficient Reinforcement Learning for Legged Robots

Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Tingnan Zhang, Jie Tan, Vikas Sindhwani
Conference on Robot Learning (CoRL)
arxiv / video /

We design a model-based framework that learns to walk using less than 5 minutes of data and generalizes to unseen tasks.

project image

NoRML: No-Reward Meta Learning

Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Jie Tan, Chelsea Finn
International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
arxiv / code / website /

We introduce a new algorithm for meta reinforcement learning that is more effective at adapting to dynamics changes.

project image

OpenRoACH: A Durable Open-Source Hexapedal Platform

Liyu Wang, Yuxiang Yang, Gustavo Correa, Konstantinos Karydis, Ronald S Fearing
IEEE International Conference on Robotics and Automation (ICRA)
arxiv / video / website /

We present a open-sourced, low-cost, ROS-enabled legged robot platform for research and education.


Python Environment for Unitree Robots

Building off the motion_imitation repo, I first developed a python-based framework for the A1 robot from Unitree. The framework includes a simulation based on Pybullet, an interface for direct sim-to-real transfer, and an reimplementation of Convex MPC Controller for basic motion control.

In 2021, I refactored the environment to remove unnecessary dependencies. The new environment is now available in the open-sourced repo of my CoRL 2021 paper.

Design and source code from Jon Barron's website