Zhuangjian Liu — accepted AI-conference papers

2023

Towards Optimal Design of Dielectric Elastomer Actuators Using a Graph Neural Network Encoder

Yangfan Li, Jun Liu, Wenyu Liang, Zhuangjian Liu

RA-L 2023

Dielectric elastomer actuators (DEAs), a type of “artificial muscles”, can generate significant deformations and offer speedy responses when exposed to voltage. Owing to their high electromechanical conversion efficiency and great flexibility, they have been extensively used in soft robot applicatio

Cited by 6 Source ↗Scholar ↗

2022

Simulation Data Driven Design Optimization for Reconfigurable Soft Gripper System

Jun Liu, Jin Huat Low, Qian Qian Han, Marisa Lim, Dingjie Lu, Chen-Hua Yeow +1

RA-L 2022

In the soft gripper design work, most of the designs such as gripping width and the design of finger actuator are purely based on experience, and repeated trial-and-error. In most scenarios, the designed actuators cannot achieve the best/optimized grasping performance with a specific design type. Th

Cited by 15 Source ↗Scholar ↗