Papers
11
Total Citations
82
H-Index
6
About
Takuya Ikeda is a robotics researcher whose work spans the full spectrum from fundamental control theory to cutting-edge computer vision for robotic manipulation. His early research established a strong foundation in robot dynamics and control, focusing on high-sample-rate control systems and the modeling of constrained motion for tasks like grinding, where he developed algebraic relations for force/position control. He also made notable contributions to mobile manipulation, addressing the critical challenge of object slippage during travel through dynamical modeling and asymptotic stability control. In a practical vein, Ikeda developed a high-efficiency glass cleaning robot equipped with a novel tension-sensitive electro-conductive yarn slip sensor. More recently, his work has pivoted to 6D object pose estimation, where he tackles the sim-to-real domain gap with style transfer techniques (Sim2Real, 15 citations) and introduces category-level pose estimation via geometric and semantic correspondence (GS-Pose, 10 citations). His latest contribution, ZeroGrasp (2025), combines zero-shot shape reconstruction with robotic grasping, directly addressing collision and motion suboptimality. With over 80 citations across these key works, Ikeda demonstrates a career-long commitment to bridging theoretical rigor with real-world robotic capability.
Research Focus
Key Achievements
Top Papers
- 1
- 2Sim2Real Instance-Level Style Transfer for 6D Pose Estimation15 citations · 2022
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- 6Guidance control of mobile robot preventing slipping of carrying objects6 citations · 2005
- 7ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping4 citations · 2025
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