Quantao Yang
Position: Research Assistant (Doctoral Student) School/office: School of Science and TechnologyEmail: quantao.yang@oru.se
Phone: No number available
Room: T1212
About Quantao Yang
I am a Ph.D student at the Center for Applied Autonomous Sensor Systems (AASS). I am affiliated to Wallenberg AI, Autonomous Systems and Software Program (WASP). I am also a visiting Ph.D scholar at Robot Perception and Learning lab (RPL) at UT Austin.
My main research interests are deep reinforcement learning, imitation learning and transfer learning. Currently I am working on robot skill learning and investigating how to apply reinforcement learning in continuous domains, such as contact-rich manipulation tasks.
Publications
Articles in journals
- Yang, Q. , Stork, J. A. & Stoyanov, T. (2022). MPR-RL: Multi-Prior Regularized Reinforcement Learning for Knowledge Transfer. IEEE Robotics and Automation Letters, 7 (3), 7652-7659.
- Yang, Q. , Dürr, A. , Topp, E. A. , Stork, J. A. & Stoyanov, T. (2022). Variable Impedance Skill Learning for Contact-Rich Manipulation. IEEE Robotics and Automation Letters, 7 (3), 8391-8398.
Conference papers
- Yang, Q. , Stork, J. A. & Stoyanov, T. (2023). Learn from Robot: Transferring Skills for Diverse Manipulation via Cycle Generative Networks. In: 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE). Paper presented at 19th International Conference on Automation Science and Engineering (IEEE CASE 2023), Cordis, Auckland, New Zealand, August 26-30, 2023. IEEE conference proceedings.
- Yang, Q. , Stork, J. A. & Stoyanov, T. (2022). Transferring Knowledge for Reinforcement Learning in Contact-Rich Manipulation. Paper presented at 2nd RL-CONFORM Workshop at IROS 2022, October 23, 2022.
- Yang, Q. , Dürr, A. , Topp, E. A. , Stork, J. A. & Stoyanov, T. (2021). Learning Impedance Actions for Safe Reinforcement Learning in Contact-Rich Tasks. In: NeurIPS 2021 Workshop on Deployable Decision Making in Embodied Systems (DDM). Paper presented at NeurIPS 2021 Workshop on Deployable Decision Making in Embodied Systems (DDM), (Online conference), Sydney, Australia, December 6-14, 2021.
- Yang, Q. , Stork, J. A. & Stoyanov, T. (2021). Null space based efficient reinforcement learning with hierarchical safety constraints. In: 2021 European Conference on Mobile Robots (ECMR). Paper presented at European Conference on Mobile Robots (ECMR 2021), Virtual meeting, August 31 - September 3, 2021. IEEE.
Doctoral theses, comprehensive summaries
- Yang, Q. (2023). Robot Skill Acquisition through Prior-Conditioned Reinforcement Learning. (Doctoral dissertation). (Comprehensive summary) Örebro: Örebro University.