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


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, 2022 [ BibTeX | 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, 2022 [ BibTeX | DiVA | PDF ] |
Refereed Conference and Workshop Articles
[1] |
Q. Yang, J. A. Stork and T. Stoyanov. Learn from Robot : Transferring Skills for Diverse Manipulation via Cycle Generative Networks. In 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE) 2023 [ BibTeX | DiVA ] |
[2] |
Q. Yang, J. A. Stork and T. Stoyanov. Transferring Knowledge for Reinforcement Learning in Contact-Rich Manipulation. 2022 [ BibTeX | DiVA ] |
[3] |
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) 2021 [ BibTeX | DiVA | PDF ] |
[4] |
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) 2021 [ BibTeX | DiVA | PDF ] |
Theses
[1] |
Q. Yang. Robot Skill Acquisition through Prior-Conditioned Reinforcement Learning. Örebro University, School of Science and Technology, Ph.D. Thesis, 2023 [ BibTeX | DiVA | PDF ] |