Kazuki Shin
Papers
9
Total Citations
106
H-Index
5
About
Kazuki Shin is a robotics researcher whose work spans two interconnected domains: autonomous navigation and human-robot collaboration. He is perhaps best known for his contributions to pedestrian trajectory prediction, where his 2020 paper introducing the Mutable Intention Filter and Warp LSTM framework — now with 39 citations — advanced the field by integrating human intention and behavioral patterns to forecast long-term pedestrian motion. His follow-up work on sparse interaction graphs for partially detected pedestrians (20 citations) further addressed real-world challenges in crowd-aware autonomous systems. Shin's more recent research pivots toward accessible and modular robotic systems. His Plug-And-Play Robotic Arm System (PAPRAS) and low-cost soft robotic skin demonstrate a commitment to making sophisticated robotics practical and deployable in everyday environments, including domestic settings. Innovative projects like augmenting vacuum robots with manipulator arms and developing the dual-arm quadrupedal robot Orthrus reflect his creative approach to expanding robotic capabilities through modularity. His work on multimodal teleoperation — combining speech and natural eye gaze — highlights a growing focus on intuitive human-robot interfaces. Across over 100 cumulative citations, Shin's research consistently bridges theoretical rigor with hands-on, real-world applicability.
Research Focus
Key Achievements
Top Papers
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- 4PAPRAS: Plug-And-Play Robotic Arm System13 citations · 2023
- 5What if a Vacuum Robot has an Arm?5 citations · 2023
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- 9Autonomous Object Model Acquisition with a Robotic Arm2 citations · 2023