This project targets on learning optimal variable impedance manipulation skills with human demonstrations in a residual reinforcement learning manner for contact-rich tasks, e.g. peg insertion. I managed to deploy it on a 7-axis Franka Emika robot arm by integrating deep reinforcement learning and imitation learning with Python, C++, and ROS.
This project aims to reproduce the results presented in the paper titled Learning Agile Robotic Locomotion Skills by Imitating Animals on our self-designed multi-modal quadruped robot. I was involved in this project when I was doing my research internship at Tencent Robotics X Lab and my work mainly focus on the Sim2Real part. During my internship, I managed to transfer gaits learned in simulation to the real quadruped robot with a 100% success rate.
This project aims to enable robot arms learn variable impedance manipulation skills from multiple demonstrations for pouring tasks in human-centric environments.
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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