Yuxuan Yang
Position: Postdoctoral Researcher School/office: School of Science and TechnologyEmail: yuxuan.yang@oru.se
Phone: No number available
Room: T2247
About Yuxuan Yang
I received my Ph.D. in computer science from Örebro University. My research interests lie in machine learning and robotics. In particular, I am interested in improving robot manipulation performance regarding deformable objects. I worked on designing a learning-based dynamics model for deformable linear objects so that a robot can manipulate the objects using model-based control methods. I am currently working on solving the problem of tracking deformable linear objects, which allows the robot to understand the scenario and improves the robustness in manipulation.
Research projects
Active projects
Publications
Articles in journals
- Yang, Y. , Stork, J. A. & Stoyanov, T. (2022). Learning differentiable dynamics models for shape control of deformable linear objects. Robotics and Autonomous Systems, 158.
- Yang, Y. , Stork, J. A. & Stoyanov, T. (2022). Particle Filters in Latent Space for Robust Deformable Linear Object Tracking. IEEE Robotics and Automation Letters, 7 (4), 12577-12584.
Conference papers
- Yang, Y. , Stork, J. A. & Stoyanov, T. (2022). Learn to Predict Posterior Probability in Particle Filtering for Tracking Deformable Linear Objects. In: 3rd Workshop on Robotic Manipulation of Deformable Objects: Challenges in Perception, Planning and Control for Soft Interaction (ROMADO-SI), IROS 2022, Kyoto, Japan. Paper presented at 35th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan, October 24-26, 2022.
- Yang, Y. , Stork, J. A. & Stoyanov, T. (2022). Online Model Learning for Shape Control of Deformable Linear Objects. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Paper presented at 35th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan, October 23-27, 2022. (pp. 4056-4062). IEEE.
- Yang, Y. , Stork, J. A. & Stoyanov, T. (2021). Learning to Propagate Interaction Effects for Modeling Deformable Linear Objects Dynamics. In: 2021 IEEE International Conference on Robotics and Automation (ICRA) IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, May 30 - June 5, 2021. Paper presented at IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, May 30 - June 5, 2021. (pp. 1950-1957). IEEE.
Doctoral theses, comprehensive summaries
- Yang, Y. (2023). Advancing Modeling and Tracking of Deformable Linear Objects for Real-World Applications. (Doctoral dissertation). (Comprehensive summary) Örebro: Örebro University.
Manuscripts
- Yang, Y. , Stork, J. A. & Stoyanov, T. Tracking Branched Deformable Linear Objects Using Particle Filtering on Depth Images.