Papers
32
Total Citations
590
H-Index
12
About
Jun Kinugawa is a robotics researcher whose work sits at the intersection of human-robot collaboration, robot calibration, and intelligent motion planning. His research is driven by a central question: how can robots and humans share workspaces safely, efficiently, and intuitively in real-world industrial settings. Kinugawa's most influential contribution, "Adaptive Motion Planning for a Collaborative Robot Based on Prediction Uncertainty" (2019, 125 citations), addresses the critical challenge of balancing worker safety with productivity in shared workspaces—a problem of growing urgency as collaborative robots enter factory floors. His early foundational work on the PaDY assembly task partner robot demonstrated how trajectory prediction could enable robots to anticipate human needs and deliver parts proactively, laying groundwork that evolved into sophisticated adaptive task scheduling systems driven by incremental machine learning. Beyond human-robot interaction, Kinugawa has made significant contributions to robot calibration, proposing optimal measurement configuration strategies for cable-driven robots (76 citations), and to bin-picking automation through his Point Pair Feature-based pose estimation framework (67 citations). His research also extends into physical human-robot interaction, exploring dance training robots and ergonomic gripper design. Collectively, Kinugawa's portfolio reflects a researcher committed to making robots not merely functional, but genuinely collaborative partners—a vision increasingly central to modern manufacturing and assistive robotics.
Research Focus
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Top Papers
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- 72D Object Localization Based Point Pair Feature for Pose Estimation25 citations · 2018
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