Quantao Yang

Quantao Yang

PhD student

AASS Research Center
School of Science and Technology
Örebro University
70182 Örebro, Sweden
Room T1212
Phone +46 (0)19 30 36 21
quantao.yang@oru.se

LinkedInGithubGoogle Scholar

I am a doctoral student in computer science at Örebro University. My main research interests are deep reinforcement learning, sim-to-real and transfer learning. Currently I am working on robot skill learning and investigating how to apply reinforcement learning in continuous domains, such as robotic tasks. Previously, I also worked on trajectory planning, 3d perception and visual servoing.

B. Eng. in Communication Engineering, Shandong University.
M. Sc. in Information and Automation Engineering, University of Bremen.


Publications

Journal Articles

[1] Q. Yang, J. A. Stork and T. Stoyanov. MPR-RL : Multi-Prior Regularized Reinforcement Learning for Knowledge Transfer. IEEE Robotics and Automation Letters, 7(3):7652-7659, 2022BibTeX | DiVA ]
[2] Q. Yang, A. Dürr, E. A. Topp, J. A. Stork and T. Stoyanov. Variable Impedance Skill Learning for Contact-Rich Manipulation. IEEE Robotics and Automation Letters, 7(3):8391-8398, 2022BibTeX | DiVA | PDF ]

Refereed Conference and Workshop Articles

[1] Q. Yang, A. Dürr, E. A. Topp, J. A. Stork and T. Stoyanov. Learning Impedance Actions for Safe Reinforcement Learning in Contact-Rich Tasks. In NeurIPS 2021 Workshop on Deployable Decision Making in Embodied Systems (DDM) 2021BibTeX | DiVA | PDF ]
[2] Q. Yang, J. A. Stork and T. Stoyanov. Null space based efficient reinforcement learning with hierarchical safety constraints. In 2021 European Conference on Mobile Robots (ECMR) 2021BibTeX | DiVA | PDF ]