Maksym Shcherban
Papers
1
Total Citations
18
H-Index
1
About
Maksym Shcherban is a roboticist whose research sits at the intersection of tactile sensing, motor learning, and developmental robotics. His work focuses on how robots can build internal body models through self-touch—a fundamental skill that animals acquire naturally but remains a major challenge for artificial systems. In his most cited paper, "Goal-Directed Tactile Exploration for Body Model Learning Through Self-Touch on a Humanoid Robot" (2021, 18 citations), Shcherban demonstrated how a humanoid robot can use tactile feedback from its own body to bootstrap motor coordination, enabling it to learn reaching without prior knowledge of its morphology. This early integration of touch into motor control represents a significant step toward more autonomous, adaptive robots. His contributions are particularly notable for bridging the gap between biological development and robotic learning, offering a framework where robots can discover their own bodies through active exploration. Shcherban’s work is foundational for researchers interested in embodied cognition, sensorimotor learning, and the development of self-aware robotic systems.
Research Focus
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Top Papers
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