Abraham K. Ishihara

About

Abraham K. Ishihara is a researcher whose work sits at the compelling intersection of robotics, neural network control, and human motor skill development. His primary contributions lie in advancing adaptive control for robotic manipulators, where he has pioneered the use of Radial Basis Function (RBF) neural networks to handle unknown dynamics. Notably, his 2011 paper on controlling robots with RBF networks and a dead-zone (11 citations) introduced a method to eliminate the restrictive Persistence of Excitation requirement, a significant step toward more practical, real-world robot control. He further extended this work with a fully tuned Growing RBF network, adapting centers and variances online for enhanced performance. Demonstrating a unique and impactful application of his expertise, Ishihara led a study (9 citations) showing that a robotically controlled pen, which increased effective inertia and viscosity, could measurably improve handwriting quality in children. This work bridges engineering and rehabilitation, highlighting the therapeutic potential of haptic robotics. His research on modeling error-driven control and stochastic stability in neural-net robot controllers further underscores his commitment to creating robust, theoretically grounded systems for uncertain environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Control of robots using radial basis function neural networks with dead‐zone

   11 citations · 2011
2. 2
   Increasing Viscosity and Inertia Using a Robotically Controlled Pen Improves Handwriting in Children

   9 citations · 2009
3. 3
   Modeling Error Driven Robot Control

   4 citations · 2009
4. 4
   Robot control with a fully tuned Growing Radial Basis Function neural network

   3 citations · 2011
5. 5
   Stochastic stability of a neural‐net robot controller subject to signal‐dependent noise in the learning rule

   2 citations · 2009

Key Collaborators

Contact & Links