I am currently a master’s student in Robotics, Systems and Control at ETH Zurich, Switzerland. Before that, I received my bachelor’s degree in Mechanical Engineering with Excellent Graduate Honor as a two-time National Scholarship winner from Tongji University, Shanghai, China.
Since January 2023, I have been working on structured skill representation for humanoids at the Biomimetic Robotics Lab at Massachusetts Institute of Technology with Prof. Sangbae Kim. Prior to that, I was engaged in agile skill imitation from limited demonstrations for legged systems with Dr. Georg Martius at the Autonomous Learning Group at Max Planck Institute for Intelligent Systems (MPI-IS), Tübingen, Germany.
My knowledge of legged robotic control using reinforcement learning was developed and refined during my work on hierarchical skill integration for ANYmal with Prof. Marco Hutter at Robotic Systems Lab (RSL), ETH Zurich, Switzerland. Before that, I was also honored to work on AgnathaX with Prof. Auke Ijspeert at Biorobotics Laboratory (BioRob), EPFL, Switzerland.
My research interests mainly focus on incorporating cutting-edge machine learning approaches into robotic control, especially for developing agile and robust skills for legged systems using reinforcement learning techniques.
For more information, please refer to my latest Resumé.
MSc in Robotics, Systems and Control
BEng in Mechanical Engineering
In this work, we propose a cooperative generative adversarial method for obtaining controllable skill sets from unlabeled datasets containing diverse state transition patterns.
In this work, we propose a generative adversarial method for inferring reward functions from partial and potentially physically incompatible demonstrations for successful skill acquirement where reference or expert demonstrations are not easily accessible.
I Learned and Grew up with
AgnathaX is an undulatory swimming robot equipped with distributed force sensors to study the neural mechanisms controlling locomotion in the spinal cord.
ANYmal is optimized for automated industrial inspection tasks with a wide range of sensors.
Mini Cheetah is a lightweight, high-power quadruped robot designed for extraordinarily agile motions.
Solo is an open-source light-weight research quadruped robot that allows highly dynamic actuations.
Spot is an agile mobile robot that navigates complex terrains for automated routine inspection and data capture.
SuperMegaBot is a 4-wheeled mobile robot for automonous site inspection and artifact detection.