M. Yamamura

About

M. Yamamura is a pioneering researcher in assistive robotics and intelligent learning systems, with a career spanning from foundational machine learning methods to impactful real-world robotic applications. Their most recognized contribution is the design and experimental validation of a novel wheelchair-stretcher assistive robot (2019, 27 citations), a transformative device that addresses the critical societal challenge of caring for disabled and semi-disabled elderly populations. By integrating mecanum wheel technology, this robot enables seamless patient transfer and enhanced physiological care, directly improving quality of life. Earlier in their career, Yamamura made significant theoretical contributions to robotics and artificial intelligence, including work on genetic algorithms for robotic search and learning (1995), and a pioneering method for reinforcement learning that incorporates prior knowledge through Bayesian networks and stochastic gradient methods (2002). This latter work advanced the field by reducing the trial-and-error burden in real-world learning applications. Yamamura’s research trajectory—from knowledge-based reinforcement learning to tangible assistive technology—demonstrates a rare and valuable ability to bridge theoretical innovation with practical, life-changing engineering solutions.

Research Focus

Key Achievements

Top Papers

1. 1
   Analysis, Design, and Experimental Research of a Novel Wheelchair-Stretcher Assistive Robot

   27 citations · 2019
2. 2
   Learning in robotics. Search and Learning by Genetic Algorithms.

   4 citations · 1995
3. 3
   Reinforcement learning with knowledge by using a stochastic gradient method on a Bayesian network

   2 citations · 2002

Key Collaborators

Contact & Links