Kazuma Sasaki
Papers
3
Total Citations
251
H-Index
3
About
Kazuma Sasaki is a leading researcher at the intersection of cognitive robotics and deep learning, with a primary focus on enabling humanoid robots to perform complex, real-world manufacturing tasks. His most influential contribution is the development of a practical, machine-learning-based system for humanoid robot workers, detailed in his highly cited 2016 paper (235 citations). This work introduced an intuitive data collection method that allows robots to learn and reliably repeat folding tasks on a production line, bridging the gap between theoretical robotics and industrial application. Sasaki’s research also explores visual-motor integration, as demonstrated in his 2015 study on neural network models for robot drawing behavior. This work enables robots to learn drawing sequences unsupervised by associating motion with visual input, creating reusable memory for task execution. His 2022 overview of machine learning for cognitive robotics further solidifies his role in shaping the field. With a career marked by practical, high-impact solutions, Sasaki is a key figure in advancing humanoid robots from lab experiments to viable factory workers.
Research Focus
Key Achievements
Top Papers
- 1Repeatable Folding Task by Humanoid Robot Worker Using Deep Learning235 citations · 2016
- 2
- 3Machine Learning for Cognitive Robotics3 citations · 2022