Hideaki Kawano
Papers
2
Total Citations
7
H-Index
2
About
Hideaki Kawano is a robotics researcher whose work centers on tactile perception and computer vision for autonomous systems. His key contributions lie in advancing robot interaction with physical environments through sensor-based object recognition and manipulation. Notably, his 2018 paper on "Object Shape and Force Estimation using Deep Learning and Optical Tactile Sensor" (5 citations) pioneered the integration of deep learning with optical tactile sensing, enabling robots to simultaneously estimate an object's shape and the forces applied during contact—a critical capability for delicate tasks in healthcare and manufacturing. Earlier, Kawano developed a SIFT feature-based template matching method for object detection and counting in life spaces (2010, 2 citations), addressing the challenge of autonomous robots operating in unstructured environments like nursing care facilities. This work laid groundwork for robots to autonomously count and track objects, a fundamental skill for inventory management and assistive robotics. While his citation counts reflect a focused, emerging impact, Kawano's research bridges the gap between tactile sensing and practical robotic autonomy, with potential applications in elderly care and medical robotics where precise, gentle interaction is paramount.
Research Focus
Key Achievements
Top Papers
- 1
- 2