Yunjie Yang

Yunjie Yang is the Senior Lecturer (Associate Professor) and Chancellor’s Fellow in Data Driven Innovation at the School of Engineering, The University of Edinburgh, UK. His research interests are in the areas of sensing and imaging for medical, robotics, and energy engineering, and machine learning in inverse problems. His research has led to over 100 high-impact journal and conference publications and has received wide media coverage. He serves as the Associate Editor of IEEE Transactions on Instrumentation and Measurement and IEEE Access, and the Guest Editor of IEEE Sensors Journal and Chemosensors. He is the recipient of the 2015 IEEE I&M Society Graduate Fellowship Award and several best paper/poster awards.

Many robotic tasks require knowledge of the exact 3D robot geometry. However, this remains extremely challenging in soft robotics because of the infinite degrees of freedom of soft bodies deriving from their continuum characteristics. Previous studies have achieved low proprioceptive geometry resolution (PGR), thus suffering from loss of geometric details (for example, local deformation and surface information) and limited applicability. This talk will introduce an intelligent stretchable capacitive e-skin to endow soft robots with high PGR bodily awareness. The e-skin can finely capture a wide range of complex 3D deformations across the entire soft body through multi-position capacitance measurements. The e-skin signals can be directly translated to high-density point clouds portraying the complete geometry via a deep architecture based on transformer. This high PGR proprioception system can assist in solving fundamental problems in soft robotics, such as precise closed-loop control and digital twin modelling.