Papers
11
Total Citations
128
H-Index
7
About
Joongbae Kim is a robotics researcher whose work spans industrial automation, human-robot interaction, and affective computing. His research has made notable contributions across three interconnected domains: robotic vision systems, dual-arm robot programming, and emotionally intelligent robots. Kim's work in industrial robotics has been particularly impactful. His vision-based object recognition systems—applied to bin picking and cell manufacturing—have helped address longstanding challenges in automated factories, including variable illumination and diverse object geometries, accumulating over 55 citations across related publications. His dual-arm robot teaching framework, which leverages exoskeleton motion capture for master-slave teleoperation, tackled the complex synchronization challenges that traditional teach pendants cannot handle, earning 21 citations and inspiring follow-up hardware work on exoskeletal master devices. Earlier in his career, Kim explored the frontier of emotionally expressive robotics, drawing on neuroscience and cognitive science to develop dynamic affective systems capable of nuanced emotional expression. His 2006 paper on neurocognitive affective systems garnered 17 citations and helped lay groundwork for more human-friendly service robots. Together, his contributions reflect a career dedicated to making robots smarter, more capable, and more naturally interactive with humans.
Research Focus
Key Achievements
Top Papers
- 1
- 2Object recognition for cell manufacturing system20 citations · 2012
- 3Neurocognitive Affective System for an Emotive Robot17 citations · 2006
- 4Exoskeletal master device for dual arm robot teaching17 citations · 2017
- 5Vision-based bin picking system for industrial robotics applications16 citations · 2012
- 6Robotic vision system for random bin picking with dual-arm robots10 citations · 2016
- 7Multiple objects recognition for industrial robot applications9 citations · 2013
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