Alexey M. Kozlov

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About

Alexey M. Kozlov is a leading researcher in cognitive robotics and artificial intelligence, with a focus on bridging the gap between low-level sensorimotor control and high-level symbolic reasoning. His most cited work, "Grounded spatial symbols for task planning based on experience" (2013, 12 citations), tackles the fundamental challenge of integrating continuous sensory data with discrete symbolic planning in autonomous humanoid robots. Kozlov’s key contribution lies in developing frameworks that allow robots to learn grounded spatial representations from experience, enabling adaptive and intelligent task execution without relying on pre-programmed models. By linking perception and action through experience-based symbol grounding, his research advances robust robot autonomy in dynamic environments. His work is particularly influential in the fields of cognitive architectures and embodied AI, where the representational gap remains a critical hurdle. Kozlov’s achievements include pioneering methods that combine machine learning with classical planning, offering practical pathways for robots to reason about space and tasks in real-time. His research continues to shape how autonomous systems perceive, plan, and act in the physical world.