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About

Junyue Dai is a leading researcher at the frontier of soft robotics, specializing in intelligent grasping, variable stiffness mechanisms, and micro- and nanomanipulation. His most impactful contribution is the development of a variable stiffness soft gripper based on rotational layer jamming, a novel approach that overcomes the limitations of traditional layer jamming units—such as complex fabrication and diminished stiffening effects. This work, published in 2023, has already garnered 30 citations, highlighting its significance in the field. Dai further advanced soft robotics by integrating reinforcement learning to enable robust grasping during high-speed motion, addressing the critical challenge of controlling nonlinear soft grippers for industrial pick-and-place tasks (13 citations). His research also explores the synergy between robotics and microfluidics for intelligent micro- and nanomanipulation, opening new avenues for applications in chemistry, materials, biology, and medicine. Additionally, Dai has pioneered stiffness control methods for soft robotic fingers using reinforcement learning, moving beyond traditional physical models. With a growing body of work that bridges fundamental design innovations and practical control strategies, Junyue Dai is shaping the future of adaptive, safe, and high-performance robotic systems.