Keynote Speaker I

Prof. Hoshang Kolivand
School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK

Abstract: In this talk, we delve into the profound impact of AI on Mixed Reality, uncovering the latest advancements and groundbreaking innovations that are breaking the boundaries of digital experiences. From sophisticated real-time simulations to personalized virtual environments, explore how AI's integration with Mixed Reality is driving unprecedented immersion and transforming the way we perceive and interact with the virtual world. Join us as we unravel the limitless possibilities and implications of this transformative fusion.

Title: Breaking Boundaries with AI: Current and Future of Mixed Reality

SHORT BIO-DATA: Hoshang Kolivand is an Assoc. Prof in AI and Mixed Reality at Liverpool John Moores University (LJMU). With an MSc degree in Applied Mathematics and Computer Science, a PhD and a Postdoc in Augmented Reality, he is a leading expert in these fields. As the Head of the Applied Computing Research Group at LJMU, Dr. Kolivand leads a team of over 35 researchers, focusing on AI and Augmented Reality. He has published extensively with over 190 papers in international journals and has presented at numerous conferences. Dr. Kolivand is a Senior Member of the IEEE and has served as a keynote speaker at more than 85 international conferences. He has organized over 30 conferences in AR, VR, AI, and HCI. In addition to his academic contributions, Dr. Kolivand has authored book chapters and several products which received over 14 awards for his work in Virtual Reality and Augmented Reality. As a dedicated researcher and educator, Dr. Hoshang Kolivand plays a significant role in advancing AI and Mixed Reality technologies, making valuable contributions to the field through his expertise and leadership.

Keynote Speaker II

Dr. Pengcheng Liu 
Department of Computer Sciences, University of York, Heslington, UK

Abstract: Robotics and AI are identified as key growth areas across the world. The robots of today are no longer confined to structured environments, nor are they completely isolated from humans. Biological systems (humans/animals) naturally exhibit energy-efficient, robust, and adaptive behaviours in complex and contact-rich environments, whilst the existing robotic systems are still suffering from insufficient capabilities of sensory-motor and learning. Humans can perform a range of tasks with planning and excellence, but these are very difficult for robots. Motion learning is the analysis of and planning for objects moving through space. It is a part of research problems across disciplines and requires specialised treatments. Motion planning algorithms move robots safely through complicated environments, validate both the assembly and operation of multipart systems, and solve a variety of other tasks. Real-time robot motion planning has become an active yet challenging research area recently, particularly with the issues of modelling of environmental interactions, recognition and grasping of deformable objects and optimization in contact-rich scenarios. In this presentation, I will start with the needs and challenges of autonomous motion learning in contact-rich and uncertain scenarios, and then explore plausible solutions to approach these problems with some interesting applications in agriculture and manufacturing.

Title: Bio-Inspired Long-Term Motion Learning in Cluttered Environments

SHORT BIO-DATA:  Pengcheng Liu is an Associate Professor in Robotics and Applied Control and Director of the Computational Autonomous Learning Systems (CALS) Lab at the Department of Computer Science, University of York, UK. He received his PhD degree from Bournemouth University, UK in 2017. He is a key member of the Real-Time and Distributed System (RTDS) Group, YorRobots (as part of the UK-RAS Network), Institute for Safe Autonomy (ISA), and Assuring Autonomy International Programme (AAIP), where research facilities have been established to address global challenges in assuring the safety and autonomy of robotics and other systems that use artificial intelligence. His research interests include bio-inspired robotics, robot learning, applied control, and intelligent systems. He has published over 100 referred journal and conference papers in the field of robotics, applied control, and AI. He has led and been involved in research projects worth about 3 million pounds funded by UK EPSRC, Innovate UK, EU H2020, and industries. Dr Liu has strong research collaboration with partners from over 10 countries, such as China, Sweden, Netherlands, Germany, Malaysia, Brunei, Australia, Indonesia, and the UK. He is an IEEE Member and Member of the IEEE Technical Committee on Bio Robotics, Soft Robotics, Robot Learning, and Safety, Security and Rescue Robotics. He is serving on several editorial boards (ICRA, Frontiers in Robotics and AI, PeerJ Computer Science, etc.) and has served on numerous international conference organization committees.