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About

Mikhail Koptev is a roboticist whose research centers on real-time, collision-free motion generation for complex robotic systems, particularly humanoid robots. His major contributions lie in developing novel frameworks that combine neural implicit representations with dynamical systems to solve long-standing challenges in robot control. Koptev pioneered the use of neural joint-space signed distance functions (SDFs), enabling efficient and reactive collision avoidance directly in configuration space—a critical advancement for high-degree-of-freedom manipulators and humanoids. His 2022 paper on this topic has garnered 53 citations, reflecting its impact on the field. He further advanced self-collision avoidance for humanoid robots with a real-time approach published in 2021 (49 citations), and recently integrated dynamical systems with sampling-based model predictive control for reactive motion generation (2024, 20 citations). Koptev’s work is notable for its practical application on the iCub humanoid robot, demonstrating adaptive grasping, navigation, and co-manipulation in a unified dynamical system framework. His research bridges the gap between theoretical motion planning and real-world robotic dexterity, making him a key figure in the development of safer, more responsive humanoid robots.