Junya Sato
Papers
7
Total Citations
17
H-Index
3
About
Junya Sato is a robotics researcher specializing in force-controlled manipulation, trajectory generation, and precision automation for industrial applications. His work bridges the gap between human dexterity and robotic execution, with a focus on tasks requiring delicate force responses such as glue application, deburring, and electronic component insertion. Sato has pioneered the use of bidirectional long short-term memory (BiLSTM) networks to correct robot trajectories based on force feedback, enabling robots to replicate human-level precision in glue-application tasks. He has also developed iterative learning-based methods for generating robot trajectories that mimic human force responses, and proposed novel approaches for grasping position detection in bulk bolts using template matching and differential evolution—offering efficient alternatives to deep learning. His contributions to hand–eye calibration include innovative solutions using tablet computers and combined linear/nonlinear regression techniques. Sato has also advanced high-precision machining force control with voice coil motor-driven deburring equipment. While his most-cited papers each hold 2–3 citations, his work represents foundational steps in force-sensitive robotics and industrial automation, with potential for significant future impact as these technologies mature.
Research Focus
Key Achievements
Top Papers
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- 4Development of an Intelligent Power Module Inserter2 citations · 2020
- 5Hand–Eye Calibration Using a Tablet Computer2 citations · 2023
- 6Hand-Eye Calibration via Linear and Nonlinear Regressions2 citations · 2023
- 7High Precision Machining Force Control of VCM-driven Deburring Equipment2 citations · 2022