Papers
9
Total Citations
52
H-Index
4
About
Jiaqiang Liang is a rising star in soft robotics, whose work masterfully bridges bioinspiration, advanced materials, and intelligent control. His research centers on three key areas: developing novel soft actuators and artificial muscles, creating bioinspired robotic swimmers and grippers, and pioneering hybrid control strategies that combine modeling with reinforcement learning. Liang’s major contributions include the invention of a self-induced large-pitch method for coiled polymer artificial muscles, enabling giant strokes for soft robotic applications, and the design of a manta ray-inspired soft swimmer with bistable flapping wings for high-speed, multi-modal locomotion. He has also advanced control theory by proposing a hybrid model-based reinforcement learning framework for precise soft robotic arm control, and developed a bionic muscle with dual-mode sensing inspired by plant tendrils. With over 50 citations across his most-cited papers—including 17 for his 2024 work on hybrid modeling and RL control—Liang’s impact is already evident. His programmable helical hierarchy in coiled muscles and energy-efficient gripper with shape locking further showcase his ingenuity. For students and researchers, Liang exemplifies how integrating mechanics, control, and biology can unlock the next generation of soft, adaptive robots.
Research Focus
Key Achievements
Top Papers
- 1
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- 3Bionic Muscle with Dual-Mode Sensing Function Inspired by Plant Tendrils8 citations · 2025
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- 5Programmable Helical Hierarchy in Coiled Polymer Artificial Muscles4 citations · 2025
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- 9An Energy-Efficient Soft Robotic Gripper with Shape Locking and Sensing1 citations · 2024