Hankun Deng
Papers
8
Total Citations
48
H-Index
5
About
Hankun Deng is a leading researcher in bio-inspired robotics, specializing in fish-inspired underwater locomotion, soft robotics, and embodied intelligence. His major contributions center on the design, modeling, and experimental learning of swimming gaits for magnetic, modular, undulatory robots (μBot). Deng pioneered the use of reinforcement learning to optimize swimming performance, demonstrating how caudal fin stiffness affects both forward swimming and turning maneuvers—a key insight for robotic design. His work on robot motor learning revealed that robust swimming emerges with an invariant Strouhal number, highlighting frequency-modulated control in fluid-structure interactions. With over 48 citations across his most-cited papers, Deng’s impact is evident in advancing autonomous swimming robots, including the development of μBot 2.0 with onboard computing and sensing for disturbance rejection and path tracking. Notably, he explored bioinspired pressure sensing for leader-follower formation and touchless underwater wall-distance sensing via active proprioception. Deng’s research bridges theoretical fluid dynamics and practical robotics, offering a platform for systematic study of morphology, gait, and performance. His achievements are foundational for students and researchers aiming to understand and replicate efficient, adaptive locomotion in aquatic environments.
Research Focus
Key Achievements
Top Papers
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- 6Analysis and Design of an In-line Pump as a Thruster for Swimming Robots4 citations · 2018
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