Our research uncovers the Mechanics behind Adaptive Reconfiguration of various active structures, soft matters, architected materials, and many others. Based on this knowledge, we develop simulation tools, performance databases, inverse design algorithms, fabrication processes, and experimental testing procedures, for Reconfigurable and Adaptive Structures. Please see the following videos for our research highlights.
Selected Reference:
Songjie Jia, Yi Zhu, 2026, Cable Pneumatic Robot: Fabrication and Simulation, Soft Robotics. (DOI: 10.1177/21695172261442061)
Yi Zhu, Jingyi Yang, Zhongqi Fan, 2025, Deployable and load-bearing kirigami plates, Engineering Structures, 343, 121078. (doi: 10.1016/j.engstruct.2025.121078)
Yi Zhu, Evugeni T. Filipov, 2024, Large-scale modular and uniformly thick origami-inspired adaptable and load-carrying structures, Nature Communications, 15, 2353. (DOI: https://doi.org/10.1038/s41467-024-46667-0)
Yi Zhu, Evgueni T. Filipov, 2022, Harnessing interpretable machine learning for holistic inverse design of origami. Scientific Reports. (DOI: https://doi.org/10.1038/s41598-022-23875-6).
Yi Zhu, Mayur Birla, Kenn Oldham, Evgueni T. Filipov. 2020. Elastically and Plastically Foldable Electrothermal Micro-Origami for Controllable and Rapid Shape Morphing. Advanced Functional Material. 2003741. (DOI:https://doi.org/10.1002/adfm.202003741)
This is an open-access simulator that can capture the behaviors of different active structures - including origami systems, MEMS robots, mechanisms, tensegrity systems, knitting structures, etc. The architecture of this package is set up such that it suits educational purpose. Eventually, a note for this package will be published. The package already contains multiple working examples for different active structures. (See GitHub Link)
This research creates large-scale load-carrying thick origami-inspired structures for adaptable civil structures or aerospace structures.
This research creates novel robotic systems using shape morphing structures. These systems can adapt their configurations to achieve complex and programmable motions and functions.
Archived 2024
This research direction explores the potentials of achieving complex 3D geometries using micro-origmai systems to overcome the limitation of traditional micro-fabrication techniques that cannot build 3D structures directly.
Archived 2024
This research direction develops simulation framework that can capture complex behaviors in active origami assemblages.
Archived 2022
This work harnesses interpretable machine learning methods to address the challenging inverse design problem of origami-inspired systems.