Max Robotics — Global Service Robot Directory + US Certification

About

Kourosh Naderi’s research bridges robotics, artificial intelligence, and human-computer interaction, with a focus on real-time motion planning, biomechanical simulation, and human fatigue prediction. His most influential work, “RT-RRT*” (2015, 155 citations), introduced a novel real-time path-planning algorithm for dynamic environments like computer games, extending the widely used Rapidly Exploring Random Tree (RRT) approach to enable adaptive, on-the-fly navigation. This contribution has become a key reference in real-time robotics and game AI. Naderi also pioneered the use of deep reinforcement learning to predict mid-air interaction movements and arm fatigue—the so-called “Gorilla arm” effect—in his 2020 paper (63 citations), offering a low-cost method to simulate user testing without real participants. His work on sampled differential dynamic programming (SaDDP, 2016, 32 citations) established a novel connection between gradient-based control and Monte Carlo methods, advancing robot control theory. Earlier research tackled multi-robot systems for dome inspection and maintenance using potential field algorithms, while his more recent “computer-aided imagery” concept applies simulated humanoid movement to mental practice in sports, such as indoor wall climbing. Naderi’s interdisciplinary approach has shaped both theoretical and applied robotics, earning recognition for its practical impact on real-time systems and human-robot interaction.