Papers
10
Total Citations
116
H-Index
5
About
Zhiwei Liao is a leading researcher in human-robot collaboration and robot learning from demonstration, with a focus on enabling robots to acquire complex manipulation skills directly from human teachers. His core contributions lie in developing advanced frameworks that go beyond traditional kinematic learning to incorporate dynamics, stiffness, and force—critical elements for safe and dexterous physical interaction. Liao pioneered the use of Riemannian dynamic movement primitives (DMP) to learn and generalize multi-space data, including position, orientation, and stiffness, from a single human demonstration. His work on simultaneously learning motion, stiffness, and force, and on ergonomic human-robot collaboration frameworks, has garnered significant attention, with his top-cited paper accumulating 32 citations. Beyond skill learning, Liao has made notable contributions to medical robotics, including a novel ankle rehabilitation robot based on screw theory and constrained 3-PSP topology, and to industrial manipulation with friction-aware peg-in-hole assembly. His innovative inverse kinematic solutions for 6R manipulators using screw theory further demonstrate his versatility. With over 130 total citations and multiple recent publications in top venues, Liao is shaping the future of intuitive, adaptive, and physically capable robotic assistants.
Research Focus
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Top Papers
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