Yuval Zaidel

Papers

Total Citations

H-Index

About

Yuval Zaidel is a pioneering researcher at the intersection of neuromorphic computing and assistive robotics. His work focuses on developing energy-efficient, adaptive control systems that mimic biological neural processing to enhance robotic performance. Zaidel’s major contributions include the first neuromorphic implementation of inverse kinematics and PID control for robotic arms, demonstrating superior robustness to perturbations and real-time adaptation compared to conventional methods (40 citations). He further advanced the field by integrating neuromorphic velocity readings with online learning for wheelchair-mounted robotic arms, significantly reducing power consumption and cost while maintaining responsive, adaptive assistance for individuals with upper extremity disabilities (21 citations). His research directly addresses the critical barriers limiting the adoption of assistive technologies—namely, high energy demands and expense. By leveraging spiking neural networks and the Neural Engineering Framework (NEF), Zaidel has shown that neuromorphic control can outperform traditional paradigms in real-world robotic applications. His work holds transformative potential for affordable, low-power assistive devices, making him a key figure in the growing field of neuromorphic robotics.