Boyko Iliev
Papers
23
Total Citations
364
H-Index
10
About
Boyko Iliev is a leading researcher in robotic manipulation, with a primary focus on **grasping, programming by demonstration (PbD), and dexterous hand control**. His work bridges the gap between human demonstration and robotic execution, enabling robots to learn complex tasks intuitively. Iliev pioneered the use of **time-clustering and fuzzy modeling** to segment and recognize human grasps from data glove recordings, a method that allows anthropomorphic hands to replicate natural grasping behaviors. His research on **independent contact regions** advanced the computational efficiency of force-closure grasps, addressing real-world positioning inaccuracies in robotic hands. With over **290 total citations**, his most influential work, "Demonstration-based learning and control for automatic grasping" (64 citations), established a framework for translating human motion into robotic programs. Iliev also contributed to **minimum-time sliding mode control** for manipulators, tackling nonlinear dynamics and uncertainty. His notable achievements include developing task-primitive-based PbD for industrial pick-and-place operations and comparing grasp recognition methods using Hidden Markov Models. For students and researchers, Iliev’s work offers foundational insights into making robots learn from humans, blending control theory with machine learning to create more adaptable, intelligent manipulation systems.
Research Focus
Key Achievements
Top Papers
- 1Demonstration-based learning and control for automatic grasping64 citations · 2008
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- 4Recognition of human grasps by time-clustering and fuzzy modeling36 citations · 2008
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- 8Independent Contact Regions based on a patch contact model14 citations · 2012
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- 10Minimum-time sliding mode control of robot manipulators11 citations · 2002