Incheol Kim
Papers
8
Total Citations
57
H-Index
5
About
Incheol Kim is a leading researcher in autonomous service robotics, with a focus on software architectures and machine learning for robotic manipulation in human environments. His most cited work, "A Software Architecture for Service Robots Manipulating Objects in Human Environments" (2020, 20 citations), introduces a framework enabling robots to perform tasks autonomously without prior knowledge, a critical step toward practical home and workplace assistants. Kim’s innovative "Hybrid Imitation Learning Framework for Robotic Manipulation Tasks" (2021, 11 citations) combines behavior cloning and state cloning to significantly improve the efficiency of robot task learning, advancing the field of robot skill acquisition. Beyond manipulation, his research spans gesture recognition for smartphone-controlled robots (2013, 7 citations) and hybrid control architectures for surveillance systems (2011, 6 citations), demonstrating versatility in human-robot interaction. Kim has also contributed to spatial reasoning and context-aware robotics, developing spatio-temporal ontology frameworks for indoor service robots (2018, 5 citations). With a career marked by foundational contributions to autonomous manipulation and imitation learning, Kim’s work directly impacts the development of intelligent, adaptable service robots for real-world environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Hybrid Imitation Learning Framework for Robotic Manipulation Tasks11 citations · 2021
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- 5
- 6Qualitative Spatial Reasoning with Directional and Topological Relations4 citations · 2015
- 7Particle Filters for Positioning WiFi Device Users2 citations · 2012
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