Prof. Li Cheng, University of Alberta, Canada

Speech Title: 3D Visual Character Motion Generation, Reconstruction, and Embodied Agents

Abstract: Recent advancements in sensing and deep learning have unlocked exciting possibilities for the visual analysis of human and animal motions in the physical 3D space. These innovations hold great potential for applications across diverse domains, including for example natural user interfaces, AR/VR, robotics, and gaming. In this talk, I will present the latest research progress in this rapidly evolving field including especially 3D human motion generation, pose tracking and shape reconstruction, and related tasks - highlighting key developments from the past few years as well as contributions from our own work.

Biography: Li CHENG is a full professor with the Department of Electrical and Computer Engineering, University of Alberta. He is currently an associate editor of IEEE Transactions on Image Processing. Prior to joining University of Alberta, he worked at A*STAR, Singapore, TTI-Chicago, USA, and NICTA, Australia. His current research interests include computer vision, multimedia data analytics, and applications. He has over 200 papers in peer-reviewed journals and conferences. His papers have been nominated for Best Paper Award at CVPR 2021. More recent details can be found at his lab website, https://vision-and-learning-lab-ualberta.github.io/.

Dr. Mojtaba Ahmadieh Khanesar, University of Nottingham, UK

Speech Title: High Precision Calibration, and Control of Industrial Robots using Advanced Optical Metrology

Abstract: Joint-angle encoders play a crucial role in achieving maximum motion resolution in industrial robots, as their accuracy directly affects overall performance and the uncertainties in the robots' geometric parameters. To improve the motion resolution of industrial robots, mechanical modifications can be applied. Advanced optical metrology equipment allows for enhanced precision by introducing a feedback loop that measures the robot's Cartesian position in real time. Laser trackers represent cutting-edge measurement technology in three-dimensional space, utilising principles of laser interferometry, multiple high-precision encoders, and internal control loops to deliver highly accurate measurements across a large volume. The feedback obtained from the laser tracker is used to control the industrial robot in real time. Additionally, the robot's forward kinematic Denavit–Hartenberg parameters are calibrated offline using position measurements.

Biography: Dr. Mojtaba Ahmadieh Khanesar (PhD, MIET’20, SMIEEE’16, MASME’23) is a research fellow in optical metrology with machine learning in the Department of Mechanical, Materials, and Manufacturing Engineering at the University of Nottingham, UK. With extensive international experience in Denmark, Turkey, Iran, and the UK, his research spans metrology, robotics, control systems, and machine learning. He earned his PhD in Control Engineering from K. N. Toosi University of Technology, Tehran, Iran. He has contributed significantly to EPSRC-funded projects, including Robodome, HARISOM, and Chattyfactories, and have supervised two PhD and undergraduate students while working at the University of Nottingham. His current project is Robodome, working on multi-illumination system for precise aerospace measurements. He serves as an associate editor for Human-Centric Intelligent Systems, Complex and Intelligent Systems, and Energies, and contribute to special issues on robust control and electromechanical systems. His research output includes 110 documents that have been cited more than 3000 times, with an h-index of 25, reflecting his significant impact in the field. He has received multiple honours, including the Collaborate to Innovate Award, top student awards, and top paper recognitions in IEEE conferences and Robotics journals, and he maintains active memberships in IEEE, ASME, IET, and BCS, demonstrating leadership and influence in the engineering and computational intelligence communities worldwide.