Papers
9
Total Citations
52
H-Index
5
About
Hideki Koike is a leading researcher at the intersection of robotics, human-computer interaction, and mixed reality, with a central focus on robot teaching and human augmentation. His major contributions lie in developing intuitive frameworks for robots to learn complex manipulation tasks from human demonstration, particularly through the innovative concept of "task-grasping"—where a robot selects a grasp not just for stability, but for its strategic advantage in completing an entire task sequence. Koike has pioneered the use of object affordance and multimodal cues (text, gaze, contact web status) to guide grasp-type recognition, making robot teaching more natural and effective. His work on the ASTRE technique for modular soft robots with variable stiffness addresses a critical barrier to entry in soft robotics by simplifying fabrication. With a cumulative citation impact exceeding 50, Koike’s research is shaping the future of human-harmonized information environments, where sensing and controlling human gaze in daily living spaces enables seamless human-robot collaboration. His notable achievements include advancing skill acquisition and transfer through augmented reality, positioning him as a key figure in creating robots that learn not just to move, but to understand context and intent.
Research Focus
Key Achievements
Top Papers
- 1Task-grasping from a demonstrated human strategy11 citations · 2022
- 2
- 3
- 4
- 5
- 6Human Augmentation for Skill Acquisition and Skill Transfer4 citations · 2021
- 7Object affordance as a guide for grasp-type recognition.4 citations · 2021
- 8Task-grasping from human demonstration3 citations · 2022
- 9SHIRI2 citations · 2012