Takayuki Murooka
Papers
8
Total Citations
50
H-Index
4
About
Takayuki Murooka is a roboticist pushing the boundaries of manipulation in complex, dynamic, and unstable environments. His research spans deformable object manipulation, whole-body control, and non-prehensile manipulation, with a particular flair for tackling tasks that are notoriously difficult for robots. Murooka’s most impactful work demonstrates sample-efficient learning for real-world manipulation of deformable linear objects, a key challenge for applications from surgical robotics to industrial assembly, achieving 22 citations. He is also known for pioneering work in robotic juggling, specifically stabilizing a diabolo—an unstable, unknown-dynamics system—by learning predictive models and developing an analytical diabolo model for simulation-to-real transfer. Beyond these, Murooka has contributed to self-repair and self-extension in robots through precise screw-tightening using CAD data, and to whole-body posture generation for force-exertion tasks like soil digging. His work on gradient-based motion planning and iterative disturbance observers further underscores his commitment to robust, real-world robotic autonomy. With a portfolio that combines theoretical modeling, learning, and practical system integration, Murooka is a rising figure in manipulation research.
Research Focus
Key Achievements
Top Papers
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- 4An analytical diabolo model for robotic learning and control4 citations · 2021
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- 8An analytical diabolo model for robotic learning and control2 citations · 2020