Prof. Jan Peters
Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt since 2011, and, at the same time, he is the dept head of the research department on Systems AI for Robot Learning (SAIROL) at the German Research Center for Artificial Intelligence (Deutsches Forschungszentrum für Künstliche Intelligenz, DFKI) since 2022. He is also is a founding research faculty member of the Hessian Center for Artificial Intelligence. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the Robotics: Science & Systems - Early Career Spotlight, the INNS Young Investigator Award, and the IEEE Robotics & Automation Society's Early Career Award as well as numerous best paper awards. In 2015, he received an ERC Starting Grant and in 2019, he was appointed IEEE Fellow, in 2020 ELLIS fellow and in 2021 AAIA fellow.
Despite being a faculty member at TU Darmstadt only since 2011, Jan Peters has already nurtured a series of outstanding young researchers into successful careers. These include new faculty members at leading universities in the USA, Japan, Germany, Finland and Holland, postdoctoral scholars at top computer science departments (including MIT, CMU, and Berkeley) and young leaders at top AI companies (including Amazon, Boston Dynamics, Google and Facebook/Meta).
Jan Peters has studied Computer Science, Electrical, Mechanical and Control Engineering at TU Munich and FernUni Hagen in Germany, at the National University of Singapore (NUS) and the University of Southern California (USC). He has received four Master's degrees in these disciplines as well as a Computer Science PhD from USC. Jan Peters has performed research in Germany at DLR, TU Munich and the Max Planck Institute for Biological Cybernetics (in addition to the institutions above), in Japan at the Advanced Telecommunication Research Center (ATR), at USC and at both NUS and Siemens Advanced Engineering in Singapore. He has led research groups on Machine Learning for Robotics at the Max Planck Institutes for Biological Cybernetics (2007-2010) and Intelligent Systems (2010-2021).
Title: Inductive Biases for Robot Reinforcement Learning
Abstract: Autonomous robots that can assist humans in situations of daily life have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. A first step towards this goal is to create robots that can learn tasks triggered by environmental context or higher level instruction. However, learning techniques have yet to live up to this promise as only few methods manage to scale to high-dimensional manipulator or humanoid robots. In this talk, we investigate a general framework suitable for learning motor skills in robotics which is based on the principles behind many analytical robotics approaches. To accomplish robot reinforcement learning learning from just few trials, the learning system can no longer explore all learn-able solutions but has to prioritize one solution over others – independent of the observed data. Such prioritization requires explicit or implicit assumptions, often called ‘induction biases’ in machine learning. Extrapolation to new robot learning tasks requires induction biases deeply rooted in general principles and domain knowledge from robotics, physics and control. Empirical evaluations on a several robot systems illustrate the effectiveness and applicability to learning control on an anthropomorphic robot arm. These robot motor skills range from toy examples (e.g., paddling a ball, ball-in-a-cup) to playing robot table tennis, juggling and manipulation of various objects.
Speaker: Prof. Jan Peters
Affiliations: Technische Universitaet Darmstadt, Germany
Speaker: Prof. Angelo Cangelosi
Affiliations: University of Manchester and Alan Turing Institute, UK
Prof. Angelo Cangelosi
Angelo Cangelosi is Professor of Machine Learning and Robotics at the University of Manchester (UK) and co-director and founder of the Manchester Centre for Robotics and AI. He holds an European Research Council (ERC) Advanced grant. He also is Turing Fellow at the Alan Turing Institute London. His research interests are in cognitive and developmental robotics, neural networks, language grounding, human robot-interaction and trust, and robot companions for health and social care. Overall, he has secured over £38m of research grants as coordinator/PI, including the ERC Advanced eTALK, the UKRI TAS Trust Node and CRADLE Prosperity, the US AFRL project THRIVE++, and numerous Horizon and MSCAs grants. Cangelosi has produced more than 300 scientific publications. He is Editor-in-Chief of the journals Interaction Studies and IET Cognitive Computation and Systems, and in 2015 was Editor-in-Chief of IEEE Transactions on Autonomous Development. He has chaired numerous international conferences, including ICANN2022 Bristol, and ICDL2021 Beijing. His book “Developmental Robotics: From Babies to Robots” (MIT Press) was published in January 2015, and translated in Chinese and Japanese. His latest book “Cognitive Robotics” (MIT Press), coedited with Minoru Asada, was recently published in 2022.
Abstract: Growing theoretical and experimental research on action and language processing and on number learning and gestures clearly demonstrates the role of embodiment in cognition and language processing. In psychology and neuroscience, this evidence constitutes the basis of embodied cognition, also known as grounded cognition (Pezzulo et al. 2012). In robotics and AI, these studies have important implications for the design of linguistic capabilities in cognitive agents and robots for human-robot collaboration, and have led to the new interdisciplinary approach of Developmental Robotics, as part of the wider Cognitive Robotics field (Cangelosi & Schlesinger 2015; Cangelosi & Asada 2022). During the talk we will present examples of developmental robotics models and experimental results from iCub experiments on the embodiment biases in early word acquisition and grammar learning (Morse et al. 2015; Morse & Cangelosi 2017) and experiments on pointing gestures and finger counting for number learning (De La Cruz et al. 2014). We will then present a novel developmental robotics model, and experiments, on Theory of Mind and its use for autonomous trust behavior in robots (Vinanzi et al. 2019, 2021). The implications for the use of such embodied approaches for embodied cognition in AI and cognitive sciences, and for robot companion applications will also be discussed.
Prof. Gianluca Antonelli
Gianluca Antonelli is Full Professor at the ``University of Cassino and Southern Lazio''. His research interests include marine and industrial robotics, multi-agent systems, identification. He has published more than 60 international journal papers and 130 conference papers, he is author of the book ``Underwater Robots'' (Springer, 2003, 2006, 2014, 2018) and co-authored the chapter ``Underwater Robotics'' for the Springer Handbook of Robotics, (Springer, 2008, 2016). From 2016 to 2021 he has been member elected of the "IEEE Robotics & Automation Society" (RAS) Administrative Committee, he has been coordinator of the EuRobotics Topic Group in Marine Robotics, he has been secretary of the IEEE-Italy section, he has been chair of the IEEE RAS Italian Chapter, he has been Chair of the IEEE RAS Technical Committee in Marine Robotics. He served in the Editorial Board of the IEEE Transactions on Robotics, IEEE Transactions on Control Systems Technology, Springer Journal of Intelligent Service Robotics. He is Fellow IEEE since 2021. Since October 2020, he is top 1% in the field "Industrial Engineering & Automation" according to common metrics and the SCOPUS database. Since December 2021, he is top 100 Italian scientists according to google scholar h-index in the category "Electronics and Electrical Engineering".
Title: Marine robotics challenges and applications. Current research at the Italian center ISME
Abstract: This talk will present the Italian interuniversity center ISME, established in 1999. ISME is composed by 9 Universities and has its background mainly in Systems and Control Engineering, Applied Mechanics and Computer Science. The activities of the ISME researchers will be presented trough brief overviews of some current and recent projects funded by National calls, the Italian Defense and the European Union. Topics will cover, among the others, underwater intervention, multiple vehicles coordination for geophysical applications, asset surveillance.
Speaker: Prof. Gianluca Antonelli
Affiliations: University of Cassino and Southern Lazio, Italy
Speaker: Prof. Angel Pasqual del Pobil
Affiliations: Jaume I University, Castellon, Spain
Prof. Angel Pasqual del Pobil
Angel Pasqual del Pobil is a Full Professor of Computer Science and Artificial Intelligence at Jaume I University (Spain), where he is the founding director of the UJI Robotic Intelligence Laboratory. He was a Visiting Professor at Sungkyungkwan University, Korea (2009-2021). He holds a B.S. in Physics and a Ph.D. in Industrial Engineering, both from the University of Navarra. He has been Co-Chair of two Technical Committees of the IEEE Robotics and Automation Society and is a member of the Governing Board of the Intelligent Autonomous Systems (IAS) Society (2012-present) and EURON (European Robotics Research Network of Excellence, 2001-2012). He has over 330 publications, including 14 books, three of them published by Springer: Robot Physical Interaction through the combination of Vision, Tactile and Force Feedback (2013), Robust motion detection in real-life scenarios (2012), and The Visual Neuroscience of Robotic Grasping (2015). Prof. del Pobil was co-organizer of over 50 workshops and tutorials in major conferences in robotics and AI. He was Program Co-Chair of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence, General Chair of five editions of the International Conference on Artificial Intelligence and Soft Computing and General Chair of the International Conference on Simulation of Adaptive Behaviour (SAB 2014). He is often Associate Editor for IEEE ICRA, IROS, RO-MAN, and ICDL conferences and has served on the program committees of over 240 international conferences, such as IJCAI, ICPR, ICRA, IROS, IAS, ICAR, etc. Prof. del Pobil has been involved in robotics research for the last 33 years, his past and present research interests include: humanoid robots, service robotics, internet robots, motion planning, mobile manipulation, visually-guided grasping, robot perception, robot physical and human interaction, robot learning, developmental robotics, and the interplay between neurobiology and robotics. Professor del Pobil has been invited speaker of 80 tutorials, plenary talks, and seminars in 14 countries. Since 2019 he is a Distinguished Lecturer of the IEEE Robotics and Automation Society. He serves as associate or guest editor for seven journals and as expert for research evaluation at the European Commission and the US National Science Foundation, among many others. His supervised Ph.D. Thesis include winner and finalists of the Georges Giralt EURON PhD Award. He has been Principal Investigator of 39 research projects.
Abstract: In 1998 I coined the term Robotic Intelligence in terms of physical interaction in the real world through perception and action. In my talk I will consider the present progress and hurdles towards full robotic intelligence, using assistant robots as a case study --since many forecasts predict a dramatic increase in this market in the coming years. For actual assistant robots to become consumer products, a leap in their robotic intelligence is called for --specially in their manipulation skills. This poses several challenges such as adaptability, autonomy, or resiliency. I will also consider the possibilities of a global intelligence by means of cloud robotics. Finally, I will compare them with robots in online shopping warehouses, with some lessons learned from our participation in the Amazon Robotics Challenge.
Prof. Haibo Liu
Haibo Liu, is professor and Ph.D. supervisor of school of mechanical engineering at Dalian University of Technology (DUT), China. He received his B.Eng. and Ph.D. degrees in Mechanical and Electrical Engineering from DLUT, in 2006 and 2012, respectively. He is IEEE member, ASME member, senior member of Chinese Society of Mechanical Engineering. He has served as the Guest Editor of the Frontiers in Mechanical Engineering, Frontiers in Materials and China Measurement & Testing Technology, and deputy secretary general of the SAC/TC22 International Standardization Working Committee. His main research interests include, Measurement-machining integrated manufacturing, On-machine measurement, Phase-change fixturing based adaptive machining, Industrial-robot aided manufacturing. He has published over 80 peer-reviewed SCI/EI journal papers like International Journal of Machine Tools and Manufacture, International Journal of Mechanical Sciences, and IEEE/ASME Transactions on Mechatronic, and over 100 authorized or pending patents. He holds over 20 major projects, including National Natural Science Foundation of China, the sub-project of the Science Challenge Project, the sub-projects of the National Key Research and Development Program and National Science and Technology Major Project of China, etc. He is the recipient of the 1st prize for Liaoning Science and Technology Progress Award (twice), the 1st prize for Science and Technology Progress Award of China Machinery Industry Federation. He was awarded the Young Changjiang Scholars Program of the Ministry of Education in 2022 and the Liaoning Provincial Outstanding Youth Fund Program in 2020.
Title: Phase-Transition Fixturing Based on MFR: Principle, Technology and Application
Abstract: The phenomenon of flutter and cutter back-off easily appear in the machining process of large complex thin-walled parts if the cutting area is not sufficiently rigid. The traditional rigid clamping method has poor adaptability to complex profiles, which can easily lead to uncontrollable deformation and vibration. Magnetorheological fluids (MRF) have the characteristics of solid-liquid transition and controllable damping, which provides a new approach for the flexible and reliable support. To achieve controllable support based on MRF, this study elucidates the macro and micro mechanical mechanisms of MRF excitation solidification under extrusion mode. The static response model of thin-walled components supported by MRF is established. To ensure the matching of support with complex external loads, the MRF support layout and magnet unit is designed. Besides, a machining vibration suppression method based on MRF damping control is proposed. Based on the above theories and technologies, an MRF support fixture for the integral head is developed, and the maximum local deformation of the part under a 50N load is 15μm. After that, experiments on knock excitation and milling of thin-walled parts is carried out. The cutting parameters are located within the stable region of the blade diagram. The results indicate that the proposed method effectively achieves modal control and vibration suppression of parts. This study supplements the theoretical basis for the performance of MRF, and provides a reference for achieving controllable support of large complex thin-walled parts.
Speaker: Prof. Haibo Liu
Affiliations: Dalian University of Technology, China
Speaker: Prof. Zhaojie JU
Affiliations: University of Portsmouth, UK
Prof. Zhaojie JU
Zhaojie Ju is Chair in Machine Learning and Robotics at the University of Portsmouth, Principle Investigator of EU Interreg Project, Director of Healthcare and Wearable Robotics Research, and Chair of IEEE SMC Portsmouth Chapter. He has attracted over €10 million research fund as PI/CoI and authored/co-authored over 250 publications in journals, book chapters, and conference proceedings (over 100 SCI-index papers). He has received 7 Best Paper Awards and 1 Best AE Award in ICRA2018. His research interests include machine intelligence, pattern recognition and their applications in human robot interaction/collaboration, robot skill learning and healthcare & wearable robotics.
Prof. Ju is an Associate Editor of several journals, such as IEEE TRANSACTIONS ON CYBERNETICS, IEEE TRANSACTIONS ON NEUROL NETWORKS AND LEARNING SYSTEMS and IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS. He is JSPS Fellow and IEEE Senior Member.
Abstract: Autonomous interaction abilities are important for robot-assisted therapy systems to assess children with autism spectrum disorder (ASD). This talk presents a multi-modal sensing system that automatically extracts and fuses sensory features such as body motion features, facial expressions, and gaze features, further assessing the children behaviours by mapping them to therapist specified behavioural classes. Experimental results show that the developed system has a capability of interpreting characteristic data of children with ASD, thus has the potential to increase the autonomy of robots under the supervision of a therapist and enhance the quality of the digital description of children with ASD. The research outcomes pave the way to a feasible machine-assisted system for their behaviour assessment.