Physical Human-Robot Interaction Symposium

Efi Psomopoulou

Research Talks and a Networking Lunch on Physical Human-Robot Interaction at Bristol Robotics Laboratory.

Bristol Robotics Laboratory

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<h1>Agenda</h1><h3>Lunch session (CRUSSH cafe area)</h3><p>12:00 - 14:30  :  Networking lunch </p><h3>Afternoon session  (BRL Seminar Room)</h3><p>14:30 - 15:00  :  Welcome and Introduction by Tactile Robotics group</p><p>15:00 - 15:40  :   Georgia Chalvatzaki</p><p>15:40 - 15:55  :  Coffee break</p><p>15:55 - 16:35  :  Zoe Doulgeri</p><p>16:35 - 17:15  :  Zhibin (Alex) Li</p><p>17:15 - 18:00  :  Panel discussion</p><p></p>

<h2>Robot Learning for Intelligent Assistants - <em>Georgia Chalvatzaki</em></h2><p>Societal facts like the increase in the elderly population, the lack of nursing staff, the hectic rhythms of everyday life, and the recent unprecedented situation of the Covid-19 pandemic, make intelligent robotic assistants more urgent than ever. The embodied AI robotic assistants are at the epicenter of modern robotics and AI research, spanning their applications from domestic environments to hospitals, workhouses to agricultural development, etc. In this talk, I will focus on sub-problems of the field that I have addressed over the last years, namely, covering the topics of human activity recognition and understanding; the combination of classical and machine learning methods for robot action planning and control; reinforcement learning methods for adaptive Human-Robot Interaction (HRI); and methods for learning to plan long-horizon tasks. Finally, I will present the open research problems that my group iROSA and I will address in the following years to enable mobile manipulation robots to operate in dynamic, unstructured environments while focusing on safety in HRIs.</p><h2>Bio</h2><p>Dr. Georgia Chalvatzaki is an Assistant Professor and the research leader of the intelligent robotic systems for assistance (iROSA) group at TU Darmstadt. She received the Emmy Noether grant from the German Research Foundation (DFG) in 2021. In iROSA, her team researches the topic of "Robot Learning of Mobile Manipulation for Intelligent Assistance," investigating novel methods for combined planning and learning to enable mobile manipulator robots to solve complex tasks in house-like environments with the human-in-the-loop of the interaction process. She is co-chair of the IEEE RAS technical committee of Mobile Manipulation and co-chair of the IEEE RAS Women in Engineering Committee. She has been voted the “AI-Newcomer” for 2021 by the German Information Society. Before that, she was a Postdoctoral researcher from October 2019 to February 2021 at the Intelligent Autonomous Systems (IAS) group of Prof. Jan Peters at TU Darmstadt. She completed her Ph.D. in 2019 on the topic "Human-Centered Modeling for Assistive Robotics: Stochastic Estimation and Robot Learning in Decision-Making."  at the Intelligent Robotics and Automation Lab of the National Technical University of Athens, advised by Prof. Costas Tzafestas and Prof. Petros Maragos.</p><h3>Lab website:  <a href="https://irosalab.com/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">iROSA lab</a></h3>

<h2>Control methods for physical human-robot interaction applications - <em>Zoe Doulgeri</em></h2><p>Over the last decades industrial robots have been widely deployed in the manufacturing industry, relieving workers from repetitive, unhealthy or arduous jobs with their workspace being strictly separated from that of humans for safety. Collaborative robots is a new generation of industrial robots that can work along side and with humans as co-workers or assistants. These robots are expected to provide flexibility by extending robot applications beyond the isolated structured work cells.  Collaborative robots should provide assistance to humans by reducing their physical and/or cognitive load, while being safe during their co-existence and collaboration with humans. In this talk, we will present shared control applications for facilitating the human towards the kinesthetic teaching of a task, avoiding forbidden regions, as well as co-manipulating objects and control methods for compliant robots to ensure human safety  under unintentional contacts while accurately following a tarjectory to reach a target pose.</p><h2>Bio</h2><p>Zoe Doulgeri is a Professor of Robotics and Control of Manufacturing Systems in the Automation and Robotics Lab of the Department of Electrical and Computer Engineering of the Aristotle University of Thessaloniki (AUTH). She received the diploma of Electrical Engineering from AUTH and the M.Sc. (DIC) in Control Systems, the M.Sc.(DIC) in Social and Economic Aspects of Science and Technology in Industry and a Ph.D in Mechanical Engineering, from the Imperial College, London, UK. She teaches Control Systems and Robotics. She authored more than 150 publications in peer-reviewed international journals and conferences. She is an evaluator of EU research and innovation proposals in Horizon-Europe and a reviewer in the most important robotics journals and conferences. She served as associate editor in the Journal of Intelligent and Robotic systems and the IEEΕ Robotics and Automation Letters. She has participated in many robotics projects financed by the EU and the Greek government. She is currently the coordinator of two H2020 European projects, and two national projects, an ESPA infrastructure project from the Central Macedonia region and a Research-Create-Innovate project. Her current research interests include the topics of physical human robot interaction, robot teaching and learning by demonstration, bimanual mobile robots, object grasping and manipulation with analytical and data based learning methods and the control of uncertain robotic systems. She has been the representative of Greece in the European Control Association from 2016-2021. She is a senior member of the IEEE, the IEEE Robotics and Automation Society and the IEEE Control Systems Society.</p><h3>Lab website: <a href="https://arl.ee.auth.gr/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Automation and Robotics Lab</a></h3>

<h2>Learning Adaptive Motor Skills for Dexterous Manipulation and Grasping - <em>Zhibin (Alex) Li</em></h2><p>This talk will focus on machine learning based approaches for achieving autonomous manipulation and grasping capabilities. I will cover the topics of (1) learning reactive reaching and grasping of flying objects; (2) learning bimanual manipulation with direct sim2real transfer; (3) learning from real data of a handful of human demonstrations; and (4) how novel tactile sensors with soft embodiment design can help learning dexterous and fine motor skills without the need of computationally intensive simulations.</p><p>Lastly, for extremely challenging real-world scenarios requiring contextual understanding and situational awareness, I will present a mixed autonomy approach which is built on optimization-based motion adaptation and can achieve collaborative bimanual tasks for multiple human operators. In the end of the talk, we will have more interactive discussions about future research directions and how researchers from different disciplines can collaborate to explore new research areas.</p><h2>Bio</h2><p>Zhibin (Alex) Li is an Associate Professor at the University College London and leads the Advanced Robotics Intelligence Laboratory. His work in 2019-2020 - "multi-expert learning of adaptive legged locomotion" - is the first implementation of a multi-expert learning architecture for adaptive quadrupedal locomotion on a real robot (selected as the cover for December 2020 issue of Science Robotics).</p><p>His research interest covers dynamic motion skills of locomotion and manipulation, optimization-based motion planning and control, and deep reinforcement learning for robot motor learning including locomotion, manipulation and grasping. He led the theme of "Shared and Autonomous Manipulation" in joint cross-hub demonstration of the UK National Robotics and Artificial Intelligence Hubs, and is working to develop novel optimization and learning based robot control to achieve a new level of robot autonomy and intelligence.</p><h3>Lab website: <a href="https://advanced-intelligent-robotics-lab.gitlab.io/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Advanced Intelligent Robotics Lab</a></h3>

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Physical Human-Robot Interaction Symposium

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