IEEE ICMA 2027 Conference

Plenary Talk 2

Learning-Based Sensing and Control of Deformable
Surfaces in Soft Robotics

Charlie C.L. Wang, Ph.D.
Professor and Chair of Smart Manufacturing
Department of Mechanical and Aerospace Engineering
The University of Manchester
Email: charlie.wang@manchester.ac.uk
http://personalpages.manchester.ac.uk/staff/charlie.wang

Abstract:
In this talk, I will present our recent advances in bridging geometric computing, computational fabrication, and machine learning to solve the dual challenges of sensing and controlling continuous deformable surfaces. First, we discuss a novel correspondence-free, function-based sim-to-real learning framework for deformable surface control. By parameterizing continuous surface deformations into functional spaces, this approach eliminates the need for strict point-to-point marker correspondences, enabling robust closed-loop shape morphing under complex physical constraints. Second, to enable real-time shape proprioception without external visual tracking, we introduce a model-free co-optimization strategy that simultaneously optimizes manufacturable flexible sensor layouts and deformation prediction algorithms. Finally, we demonstrate the practical realization of these methods on physical pneumatic soft robotic systems, including soft robotic mannequins capable of dynamic body shape morphing and soft manipulators working in confine spaces. By unifying data-driven learning with geometry-aware modeling, these techniques overcome longstanding bottlenecks in soft robotic autonomy, paving the way for next-generation applications in smart manufacturing, wearable devices, and robotic healthcare.

Charlie C.L. Wang is internationally recognized for his research in geometry-driven intelligence for design and manufacturing. His work has transformed the integration of design, analysis, and fabrication by establishing geometric computing and optimization as foundations for intelligent engineering across additive and hybrid manufacturing. He is currently Chair in Smart Manufacturing at the University of Manchester (UoM) holding an EPSRC Open Fellowship (2023-2028) on Field Computation Based Kernel for Vector 3D Printing. Before joining UoM in 2020, he worked as Professor and Chair of Advanced Manufacturing at Delft University of Technology and as Professor of Mechanical and Automation Engineering at the Chinese University of Hong Kong.

Prof. Wang has received numerous distinctions, including two ACM SIGGRAPH Asia Best Paper Awards (2022 and 2025), the ASME CIE Excellence in Research Award (2016), ten Best Paper Awards overall, five project-oriented technology innovation awards, and three teaching awards. He is Fellow of the American Society of Mechanical Engineers (ASME), and the Solid Modelling Association (SMA). His research interests include Digital Manufacturing, Computational Design, Additive Manufacturing, Soft Robotics, Geometric Computing, and Computer Graphics.