Yuxuan Yang
Position: Research Assistant (Doctoral Student) School/office: School of Science and TechnologyEmail: yuxuan.yang@oru.se
Phone: +46 19 303000
Room: T1217
About Yuxuan Yang
I am a Ph.D. student at the Adaptive and Interpretable Learning Systems Lab.
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
- Adaptive Automation for Face Drilling
- HoseProtect - Safe Remote Drilling through Predictive Modeling of Hydraulic Hoses
Completed projects
Research teams
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.