The 7th International Conference on Ubiquitous Positioning, Indoor Navigation and Location-Based Services\uff08UPINLBS 2022\uff09<\/p>\n
Brain-inspired Navigation Workshop <\/strong><\/span><\/p>\n \uff081 Keynote + 5 Invited Talks + 2 Papers\uff09<\/p>\n With the continuous expansion of the application scope of intelligent robots, many new challenges and difficulties are brought to intelligent navigation and positioning technology. However, humans and animals in nature have excellent advanced cognitive navigation capabilities including intelligent spatial positioning, mapping, navigation, memory, and decision-making. In recent years, neuroscientists have gradually discovered the “brain\u2019s GPS system”, including place cells, grid cells, head-oriented cells, etc. The neural mechanism of spatial navigation and positioning in the brain with capabilities of high-efficiency, low-energy, robustness, and super-intelligence provide many inspirations and theoretical basis for us to develop new brain-inspired intelligent navigation and positioning technologies. In recent years, by drawing on the neural mechanisms of human and biological brains such as environmental perception, spatial learning, map memory, path decision-making, and spatial reasoning, the development of novel brain-inspired navigation theories and methods and cutting-edge technologies have attracted great attention from scholars in multidisciplinary fields. The brain-inspired navigation has become one of the frontier topics in the field of neuroscience and brain-inspired intelligence, which provides a new technical idea for the development of a new generation of intelligent navigation and positioning technology, and has important scientific significance and application value.<\/p>\n Workshop Topics<\/strong>\uff1a<\/strong><\/p>\n Organizers <\/strong><\/p>\n Fuqiang Gu, Professor of College of Computer Science, Chongqing University. He got his Ph.D. from the University of Melbourne. He is a member of IEEE, ACM, and CCF. He worked at the RWTH Aachen University, University of Toronto, and the National University of Singapore. His research interests include navigation and positioning, machine learning, robotics, brain-like intelligence, etc. He has published in many leading journals and conferences such as TVT, CSUR, TIM, IoT, FGCS, IJCAI, GLOBECOM, and IROS. He is funded by NSFC, DAAD, and so on.<\/p>\n Fangwen Yu currently works as a Postdoctoral Research Fellow in the Center for Brain-Inspired Computing Research at Tsinghua University. He got his Ph.D. from the China University of Geosciences, Wuhan. He was a visiting Ph.D. student at Queensland University of Technology (QUT). He is a member of the IEEE-RAS Technical Committee on Cognitive Robotics and Neuro-robotics Systems, the Cognitive Navigation Group at the Royal Institute of Navigation (RIN), etc. His research interests include brain-inspired 3D navigation, brain-inspired robotics, brain-inspired computing system, etc. His research models the neural mechanisms in the brain underlying tasks like 3D navigation and 3D spatial cognition to develop new neuromorphic 3D SLAM and 3D cognitive navigation technologies for space, air, land, sea-based autonomous robots, and vehicles. He was awarded the Innovation Grand Prize at ICCBC 2019, the Innovation Award of 6th Hubei Province \u201cChangjiang Student\u201d, Excellent Doctoral Thesis, UPINLBS 2016 Best Paper Award, etc.<\/p>\n \u00a0<\/p>\n Invited<\/strong> Talks<\/strong><\/p>\n Title: Neuroscience-inspired SLAM and navigation: Where are we coming from and where could we go?<\/em><\/strong><\/p>\n 10:50a.m. \u2012 11:30a.m. March 18, Friday<\/p>\n Professor Milford conducts interdisciplinary research at the boundary between robotics, neuroscience and computer vision and is a multi-award winning educational entrepreneur. His research models the neural mechanisms in the brain underlying tasks like navigation and perception to develop new technologies in challenging application domains such as all-weather, anytime positioning for autonomous vehicles. He is also passionate about engaging and educating all sectors of society around new opportunities and impacts from technology including robotics, autonomous vehicles and artificial intelligence. Michael currently holds the positions of Australian Research Council Laureate Fellow, Joint Director of the QUT Centre for Robotics, QUT Professor, Microsoft Research Faculty Fellow and is a former Chief Investigator at the Australian Centre for Robotic Vision.<\/p>\n \u00a0<\/p>\n Abstract: In this talk, I will briefly overview our work in neuroscience-inspired robotic mapping and navigation, and discuss five key questions and open research areas, linking into past and recent neuroscience and biological discoveries: The Loop Closure Question, the 3D Question, the Probabilistic Question, the Multi-Scale Question, and the Behavioral Question.\u00a0<\/p>\n \u00a0<\/strong><\/p>\n Title: Cognitive mapping based on geometry representations<\/strong><\/em><\/p>\n 13:30 \u2012 13:50, March 18, Friday<\/p>\n Bailu Si, Professor of the School of Systems Science, Beijing Normal University and Chinese Institute for Brain Research Beijing. He serves as committee member of the Beijing Association of Brainnetome and Brain-inspired Intelligence, Computational Neuroscience and Neuroengineering Association of Chinese Neuroscience Society.\u00a0 His research interest focuses on computational neuroscience and brain-inspired robotics. His main work includes the computational theory of memory circuits, brain-inspired navigation systems.\u00a0\u00a0<\/p>\n \u00a0<\/p>\n Title: Brain-inspired Computing Chip and Robots<\/em><\/strong><\/p>\n 13:50 \u2012 14:10, March 18, Friday<\/p>\n Lei Deng received the B.E. degree from the University of Science and Technology of China, Hefei, China in 2012, and the Ph.D. degree from Tsinghua University, Beijing, China in 2017. He was a Postdoctoral Fellow at the Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA from 2017 to 2021. He is currently an Assistant Professor at the Center for Brain-Inspired Computing Research (CBICR), Tsinghua University, Beijing, China. His research interests span the area of brain-inspired computing, neuromorphic chip, machine learning, and computer architecture. He has published over 80 peer-reviewed journal\/conference papers including Nature (cover) series, JSSC, JSTSP (cover), PIEEE, IEEE\/ACM Transactions, ICML, ICLR, AAAI, NeurIPS, HPCA, ASPLOS, DATE, etc., and over 30 China\/US patents. He was a recipient of the 2021 Outstanding Young Scholar of NSFC, 2021 Outstanding Youth Award of CAAI, 2021 Young Scholar for Brain Research, Beijing, and 2019 MIT Technology Review Innovators Under 35 China. He served as a Guest Associate Editor for Frontiers in Neuroscience and Frontiers in Computational Neuroscience, a PC Member for IJCNN2021, ASAP2021 and ISNN2019, and a reviewer for a number of journals and conferences. His representative achievement, Tianjic neuromorphic chip and the unmanned bicycle, was published in Nature as a cover story, which was selected into the 2019 Top-10 Chinese Scientific and Technological Advances, Top-10 Chinese Scientific Advances, Top-15 World Leading Internet Scientific and Technological Achievements, and received the 2020 Leading Award of Chinese Robotics.<\/p>\n \u00a0<\/p>\n Title: Brain-inspired Navigation for UAV Swarm Working in GNSS-denied Environment<\/em><\/strong><\/p>\n 14:10 \u2012 14:30, March 18, Friday<\/p>\n Chuang Yang is currently a visiting PhD student at Department of Electrical and Computer Engineering, National University of Singapore, funded by China Scholarship Council. He is also a PhD student at College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, China. His research interests include brain-inspired navigation and decision-making for aerial vehicles, inertial-based integrated navigation, and brain-inspired computing. He is a member of Royal Institute of Navigation, Chinese Association for Artificial Intelligence, Chinese Neuroscience Society, Chinese Association of Automation, and Chinese Society of Aeronautics and Astronautics. He is also a Journal Reviewer of IEEE Transactions on Industrial Electronics, Chinese Journal of Aeronautics, and Aircraft Engineering and Aerospace Technology.<\/p>\n \u00a0<\/p>\n Title: Towards Cognitive Navigation: A Biologically Inspired Calibration Mechanism for the Head Direction Cell Network<\/em><\/strong><\/p>\n 15:10 \u2012 15:30, March 18, Friday<\/p>\n Dr. Zhenshan Bing is a senior researcher at the Technical University of Munich, Germany. His research interests include biomimetic robots, brain-inspired navigation algorithm, and reinforcement learning. He has published over 30 peer-reviewed papers on top-level journals and conferences. He severs as the project coordinator of the Sino-DFG Mobility Program and the KI.FABRIK Bavaria research and development project. He also serves as the associate editor of the journal Frontiers in Neurorobotics and Cyborg and Bionic Systems.<\/p>\n \u00a0<\/p>\n Title: Biologically Inspired Spiking Neural Networks for Energy-Efficient Robot Navigation and Control<\/em><\/strong><\/p>\n 15:30 \u2012 15:50, March 18, Friday<\/p>\n Guangzhi Tang is a Ph.D. candidate in the Department of Computer Science at Rutgers, the State University of New Jersey. His research focuses on neuromorphic computing and neurorobotics, specifically on designing energy-efficient solutions for robot navigation, control, and learning using spiking neural networks. He is a member of the Computational Brain Lab (Combra) at Rutgers University and has been interned at the Neuromorphic Computing Lab (NCL) at Intel Labs.<\/p>\n \u00a0<\/p>\n Abstract:<\/strong> Energy-efficient navigation and control are crucial for autonomous mobile robots as they explore unknown environments and solve real-world tasks with limited on-board resources. To exhibit fully autonomous behavior, mobile robots need to understand the environments by solving the localization and mapping problem and performing goal-directed control solely based on sensory observations. We propose Spiking Neural Networks (SNNs) for energy-efficient navigation and control on the neuromorphic processor. First, we present a biologically constrained SNN, inspired by the brain’s spatial system, solving the unidimensional localization and mapping problem while only consuming 1% of dynamic power compared with the conventional approach. Then, we propose a hybrid reinforcement co-learning framework that jointly trains deep neural networks and SNNs to learn optimal controls for mobile robot navigation and high-dimensional continuous control. These works pave the way towards an alternative solution for autonomous robots fully controlled by neuromorphic processors.<\/p>\n \u00a0<\/p>\n Papers<\/strong>:<\/strong><\/p>\n \u00a0<\/p>\n\n
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