Minoru Okada
Papers
1
Total Citations
7
H-Index
1
About
Minoru Okada specializes in robotic manipulation, skill transfer, and haptic-based learning systems. His major contribution lies in developing methods that enable robots to acquire complex assembly skills—such as peg-in-hole insertion—by learning from human demonstrations in virtual environments. His 2007 paper, "Intelligent Robotic Peg-in-Hole Insertion Learning Based on Haptic Virtual Environment," introduced a novel approach that combines position and force/torque data with prior task knowledge to generate executable robotic skills. This work has garnered 7 citations and laid foundational insights for haptic-guided robotic learning. Okada’s research bridges the gap between human dexterity and autonomous robotic execution, with implications for manufacturing and automation. His work is particularly notable for integrating virtual reality and haptic feedback to accelerate skill acquisition, offering a scalable path for training robots in precision tasks. For students and researchers, Okada’s contributions highlight the power of cross-modal learning—where touch and vision converge—to solve real-world manipulation challenges.
Research Focus
Key Achievements
Top Papers
- 1