Papers
8
Total Citations
124
H-Index
6
About
Gedong Jiang is a leading researcher in robot learning and human-robot collaboration, focusing on how robots can safely and effectively learn complex skills from human demonstration. His work bridges the gap between traditional kinematic-only approaches and the need for dynamic skill transfer, introducing Riemannian-based dynamic movement primitives (DMP) that enable robots to learn and generalize not just motion, but also stiffness and force—a key advancement for dexterous manipulation. Jiang’s contributions to safe human-robot coexistence are equally impactful, with a real-time hierarchical control method (15 citations) and a stochastic optimization framework for collision-free motion generation (23 citations) that uses composite signed distance fields. He has also developed a novel screw-theory-based inverse kinematic solution for 6R robots with offset joints (18 citations) and an ergo-interactive framework for one-shot collaborative skill learning (17 citations). With over 120 total citations and a steady stream of high-impact work from 2020 to 2024, Jiang is shaping the future of intuitive, safe, and dynamic human-robot interaction.
Research Focus
Key Achievements
Top Papers
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- 5A real-time hierarchical control method for safe human–robot coexistence15 citations · 2023
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