Yuxiang Yang

I'm a 4th year PhD candidate 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.

News

• (2023/10) I'm now a PhD candidate!
• (2023/09) One paper accepted to CoRL 2023.
• (2023/04) One paper accepted to L4DC 2023.

Research

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

CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller

We design a GPU-accelerated, general-purpose, hierarchical framework to learn continuous, long-distance, and adaptive jumping for quadrupedal robots.

Continuous Versatile Jumping using Learned Action Residuals

We enable omni-directional jumping and jump-turns by combining heuristic controllers and learned action residuals.

Learning Semantics-Aware Locomotion Skills from Human Demonstrations

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

Fast and Efficient Locomotion via Learned Gait Transitions

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

Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning

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

ES-MAML: Simple Hessian-Free Meta Learning

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

Data Efficient Reinforcement Learning for Legged Robots

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

NoRML: No-Reward Meta Learning

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

OpenRoACH: A Durable Open-Source Hexapedal Platform

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

Projects

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.