[WiE Webinar] Skill Learning for Human-Robot Interaction and Robot Manipulation
Friday 27 March 2026 at 2:00 PM CET, 8:00 AM ET
By Ning Wang (Sheffield Hallam University, United Kingdom)
Extraordinary Women-in-IES Webinar
Register now using the link below:
https://attendee.gotowebinar.com/register/8262396028790584926
Abstract:
Imitation learning is a method of learning through observing expert behaviours and imitating their strategies for decision-making in complex situations. It has been widely employed in robotics in recent years, and has shown the advantage of efficiently learning expert skills, including subtle adjustments of contact forces and positions in human dexterous manipulation. Expert skills inherently contain human tacit knowledge and empirical rules, and it is expected that if robots can acquire these in some form, their capabilities can be fundamentally enhanced. Robot imitation learning is based on learning human skilled movements and motor profiles, is also called Learning from Demonstration (LfD) or Programming by Demonstration (PbD), emphasizing the aspect of learning directly from human demonstrations. This talk will focus on the principles of robot imitation learning methods, in specific, dynamic system-based approach and their application in various robot learning scenarios, including human-robot collaboration, contact-rich robot manipulation, obstacle avoidance, and grasping, etc, delivering our recent research findings about robot learning in various real-world tasks.
Presenter’s bio:
Dr Ning Wang is an Associate Professor in Robotics and AI at Sheffield Hallam University, and a member of Sheffield Robotics, United Kingdom. She received the Ph.D. degree in Electronics Engineering at The Chinese University of Hong Kong in 2011.
Dr Wang’s research centres on robotics and intelligent learning, with a primary focus on human-robot interaction, embodied AI, and data analytics, aiming to develop systems that enable seamless collaboration between humans and machines, emphasizing adaptability, autonomy, and effective decision-making in dynamic environments, with applications on healthcare, autonomous driving, smart manufacturing, etc. She has been key member of EU FP7 Project ROBOT-ERA, EU Regional Development Funded Project ASTUTE2020 and industrial projects with UK companies, and has been awarded several awards including best application paper award of ICAC’24, best paper award of DISA’23, best student paper award of ICAC’23, IET premium award for best paper 2022, best paper award of ICIRA’15, best student paper award nomination of ISCSLP’10, and award of merit of 2008 IEEE Signal Processing Postgraduate Forum, etc.
Dr Wang is a Senior member of IEEE, a Fellow of British Computer Society and a Fellow of the Higher Education Academy, UK. She is Co-Chair of IEEE Robotics and Autonomous Systems (RAS) Technical Committee on Collaborative Automation for Flexible Manufacturing, an invited member of EPSRC UK Robotics and Autonomous Systems (UK-RAS) Topic Group on Human-Robot Interaction – Best practices and methods, and Turing University Network Liaison of Alan Turing Institute, UK’s national institute for data science and artificial intelligence. She is now serving as Associate Editor for IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE Transactions on Neural Networks and Learning Systems, Robot Learning, and International Journal of Social Robotics.