Max Robotics — Global Service Robot Directory + US Certification

About

Naveen Gehlot’s research career is defined by pioneering work in robotic manipulation and intelligent control systems. His foundational contributions to robot inverse kinematics introduced a modular neural network approach that assigns neural modules to each manipulator link, enabling efficient, scalable solutions for complex robotic motion. This work, cited 12 times, remains a reference point for neural-based kinematic control. Gehlot further advanced the field by systematically comparing control strategies—direct inverse neurocontrollers and nonlinear neural compensators—for SCARA manipulators, providing critical insights into neural network performance in robotic systems. His modular neurocontroller framework, applicable to arbitrary N-DOF manipulators, leveraged the recursive Newton-Euler formulation for adaptive control, demonstrating versatility across robotic architectures. More recently, Gehlot has expanded into practical robotics education with the design and analysis of a low-cost 3-DOF robotic arm, and into biomedical engineering with cutting-edge hand gesture recognition using 1D convolutional neural networks on sEMG signals. His 2025 papers on neural architecture search for gesture recognition signal a forward-looking shift toward automated, efficient human-machine interfaces. With over 30 citations across his most-cited works, Gehlot’s trajectory from foundational neural robotics to applied AI demonstrates sustained impact and adaptability.