Liangjing Yang, Associate Professor
Zhejiang University - University of Illinois Urbana-Champaign Institute, ZJUI, China
Biography: Liangjing Yang received the B.Eng. and M.Eng. degrees in Mechanical Engineering from the National University of Singapore (NUS) in 2008 and 2011, respectively. He obtained the D.Eng. degree from the University of Tokyo (UTokyo) in 2014 with a scholarship to work in the Bio-Medical Precision Engineering Laboratory. Before joining ZJU-UIUC Institute, he was with the Singapore University of Technology and Design (SUTD), and Massachusetts Institute of Technology (MIT) under a joint postdoctoral fellowship award. Liangjing is currently an associate professor at Zhejiang University-University of Illinois at Urbana-Champaign (ZJU-UIUC) Institute, appointed as the Deputy Director of Research Cluster for Intelligent Systems and Materials, and heads the Intelligent Robot Vision & Control (IRVC) Research Group.
Liangjing’s research interests are in robotics and machine vision. His previous work in UTokyo on image mapping for 3D ultrasound-guided endoscopic procedures is published in both engineering and medical journals, including an image mapping system published in Surgical Endoscopy. In NUS, he developed a robotic system for overlapping ablation of large liver tumors, which is featured in a special issue on “Surgical and Interventional Medical Devices” of ASME/IEEE Transactions on Mechatronics. In SUTD and MIT, Liangjing combines his expertise in Robotics and Machine Vision to develop a vision-guided robotic micromanipulation platform. He holds 6 Chinese and 3 US patents including an ultrasound image-guided robot and a robotic surgical training system. The former technology is successfully licensed and commercialized while the latter development was named “Best Innovation in Biomedical Application” in a design challenge by National Instrument in 2011.
Kang Wu, Associate Professor
Jiangnan University, China
Biography: Kang Wu received his B.S. degree in electrical engineering from Kwangwoon University, Seoul, Korea, M.S. degree in electrical engineering from Newcastle University, Newcastle upon Tyne, United Kingdom, Ph.D. degree in automatic control from Southeast University, Nanjing, China and Joint Training Ph.D. from Loughborough University, Loughborough, United Kingdom. He is currently an associate professor at the School of Automation and Intelligent Science, Jiangnan University. His research interests include nonlinear control, disturbance rejection control, motion control and machine learning. He has co-authored over 30 papers including SCIENCE CHINA, IEEE/CAA JAS. He is a committee member of the Artificial Intelligence and Robotics Education Committee of the Chinese Association of Automation and a member of Jiangsu Association of Automation.
Speech Title: Force Tracking under Local Geometric Uncertainty via Adaptive Impedance Control and Gaussian Process Surface Estimation
Abstract: In robotic contact tasks such as grinding and polishing, accurate normal force tracking and proper end-effector orientation are essential for ensuring processing quality. However, local deviations from the nominal surface geometry can corrupt both the contact-height and surface-normal references, thereby degrading force tracking and contact stability. To address this challenge, this paper proposes a geometry-aware adaptive impedance control framework that couples geometric reference correction with contact force tracking. Adaptive impedance control adjusts the commanded position in response to contact force errors, enabling accurate force tracking where the actual surface closely matches the nominal geometry. When contact deviations exceed acceptable limits, a local Gaussian process (GP) combines contact-position measurements with force-derived surface gradient observations, which supplement the limited normal information in position samples, to update the local surface estimate, which is then used to correct the height and normal references supplied to the adaptive impedance controller. The closed-loop stability of the proposed framework is analyzed by incorporating bounded geometric estimation errors into the force-tracking dynamics. Simulation studies involving various unmodeled local surface variations are conducted to evaluate the proposed framework in terms of contact force tracking accuracy and contact stability.