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Date
  • 12/01/2022 5:00 pm
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Fumiya Iida

Fumiya Iida

University of Cambridge, Bio-Inspired Robotics Laboratory

Fumiya Iida is a Professor of Robotics at Department of Engineering, University of Cambridge, the director of Bio-Inspired Robotics, and the deputy director of EPSRC Centre of Doctoral Training in Agri-Food Robotics. He received his bachelor and master degrees in mechanical engineering at Tokyo University of Science (Japan, 1999), and Dr. sc. nat. in Informatics at University of Zurich (2006). In 2004 and 2005, he was also engaged in biomechanics research of human locomotion at Locomotion Laboratory, University of Jena (Germany). From 2006 to 2009, he worked as a postdoctoral associate at the Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology in USA. In 2006, he awarded the Fellowship for Prospective Researchers from the Swiss National Science Foundation, and in 2009, the Swiss National Science Foundation Professorship for an assistant professorship at ETH Zurich from 2009 to 2015. He was a recipient of the IROS2016 Fukuda Young Professional Award, Royal Society Translation Award in 2017, Tokyo University of Science Award in 2021. His research interest includes biologically inspired robotics, embodied artificial intelligence, and biomechanics, where he was involved in a number of research projects related to dynamic legged locomotion, dextrous and adaptive manipulation, human-machine interactions, and evolutionary robotics.

Bio-Inspired Soft Robotics x AI

Soft robotics research has made considerable progress in many areas of robotics technologies based on deformable functional materials, including locomotion, manipulation, and other morphological adaptation such as self-healing, self-morphing, and mechanical growth. While these technologies open up many new robotics applications, they also introduced a number of challenging problems in terms of sensing, modelling, planning and control. Because of the general complexity of the system based on flexible and continuum mechanics, and a large diversity of system-environment interactions, the conventional methods are often not applicable, and the new approaches are necessary based on the state-of-the-art machine learning techniques. In this talk, I will introduce some of the research projects in our laboratory that make use of soft robotics and machine learning techniques, for addressing the complexity problems in robotic applications.