Masahito Yashima
Papers
14
Total Citations
76
H-Index
5
About
Masahito Yashima is a leading researcher in robotic manipulation and human-robot collaboration, with a focus on dexterous, nonprehensile, and learning-based control strategies. His work spans iterative learning control, impedance and admittance adaptation, and dynamic manipulation—including throwing, catching, and in-hand object handling. Yashima’s most cited paper, “Iterative learning of variable impedance control for human-robot cooperation” (2016, 17 citations), introduces a novel scheme for generating time-series impedance values to enhance human-robot cooperative tasks. He has also made foundational contributions to robotic nonprehensile catching (2014, 11 citations), demonstrating how caging and gravity can enable robust object capture without grasping. His research on arm trajectory planning (2008, 7 citations) and throwing manipulation (2010, 6 citations) further showcases his ability to combine control theory with practical robotics. More recently, Yashima has advanced human-robot collaboration through admittance learning (2023, 4 citations) and Bayesian optimization for damping fields (2022, 2 citations). With over 60 total citations across his top ten papers, Yashima’s work is essential reading for researchers interested in adaptive, learning-based approaches to robotic manipulation and physical human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1Iterative learning of variable impedance control for human-robot cooperation17 citations · 2016
- 2Robotic nonprehensile catching: Initial experiments11 citations · 2014
- 3
- 4
- 5Grasp planning based on dynamics shaping6 citations · 2011
- 6
- 7
- 8
- 9
- 10