![]() ![]() This talk introduces several SPS methods based on physical fields as an alternative or a complement to visible light which is commonly assumed as the medium in conventional machine vision. Recent advances in sensing and perception systems (SPSs), which move beyond from point measurements to field representation, enable exciting new technologies to facilitate autonomous machines capable of evolving with more and more ‘smart functions’ that ultimately make the process a self-improved system. Kok-Meng Lee, Georgia Institute of Technology, USA Myron Diftler, NASA Johnson Space Center, USA Robotic Technology Efforts at the NASA/Johnson Space Center Michael McCarthy, University of California, Irvine, USAĭesign and Application of Parallel-Parallel 6-Legged Robotsįeng Gao, Shanghai Jiao Tong University, China Gregory Chirikjian, Johns Hopkins University, USAĭesign of Robotic Systems to Trace Specified Curves Ten Technologies That Will Change Robotics, and the World, Forever Masayoshi Tomizuka, University of California, Berkeley, USAĬognitive Sensing for Robotic Dexterous Operationsīipedal Walking: A Pluridisciplinary PerspectiveĬhair: Ronald Lumia, University of New Mexico, USA Max Meng, The Chinese University of Hong Kong, Chinaīradley Nelson, Eidgenössische Technische Hochschule (ETH) Zürich, SwitzerlandĬhair: Jianwei Zhang, University of Hamburg, GermanyĮnsuring Safety of Humans and Industrial Robots Sharing the Same Workspace Micro Medical Robotics: Painless and Scarless Joel Burdick, California Institute of Technology, USA Recovery of Function in Major Spinal Cord Injury Using Spinal Stimulation and Assistive Robotics Han Ding, Huazhong University of Science & Technology, ChinaĬhair: Dong-Soo Kwon, Korea Advanced Institute of Science and Technology (KAIST), Korea Robotic Machining: Challenges and Chances Sampling-Based Motion Planning: Advances and Challenges Kok-Meng Lee, Georgia Institute of Technology, USA Chair: Marcelo Ang, National University of Singapore, SingaporeĪlternative Sensing and Perception based on Physical fields for Robotics and Automation ![]()
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