Papers
41
Total Citations
678
H-Index
17
About
Changjoo Nam is a robotics researcher whose work spans human-robot interaction, multi-robot systems, and robotic manipulation in complex environments. His research has made significant contributions across three interconnected domains: understanding and modeling human trust in autonomous systems, solving multi-robot task allocation problems under uncertainty, and developing intelligent manipulation planning algorithms for cluttered environments. Nam's most influential work examines how humans trust and interact with swarm robots operating at varied levels of autonomy, with his 2019 study accumulating 73 citations and revealing nuanced computational models of trust in supervisory control settings. Complementing this, his inverse reinforcement learning approach to predicting trust (2017) challenged conventional performance-based trust models by demonstrating that humans respond to physical robot characteristics rather than task outcomes alone. In robotic manipulation, Nam has developed a suite of algorithms addressing the challenging problem of retrieving objects from densely cluttered, confined spaces—work that has collectively garnered nearly 140 citations and advanced task-and-motion planning frameworks significantly. His multi-robot task allocation research further demonstrates his breadth, tackling cost uncertainty and resource contention problems critical for real-world robot deployments. Across more than a decade of research, Nam has consistently bridged theoretical rigor with practical robotic applications, establishing himself as a versatile and impactful contributor to modern robotics.
Research Focus
Key Achievements
Top Papers
- 1Models of Trust in Human Control of Swarms With Varied Levels of Autonomy73 citations · 2019
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- 5Fast and resilient manipulation planning for target retrieval in clutter39 citations · 2020
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- 9VPass: Algorithmic compass using vanishing points in indoor environments28 citations · 2009
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