Papers
2
Total Citations
32
H-Index
2
About
Olga Tolochko’s research lies at the intersection of robotics, tactile sensing, and neural computation, where she develops brain-inspired controllers that enable robots to perceive and interact with their environment more naturally. Her major contributions center on integrating artificial skin with adaptive control systems, allowing robotic arms to adjust their compliance in real time based on touch input. In her highly cited 2016 work, she introduced a neural learning framework for touch-based admittance control, enabling a robot to modulate its stiffness in four directions using tactile feedback—a critical step toward safe, intuitive human-robot interaction. Expanding on this, her 2019 study pioneered a brain-inspired coding scheme for robot body schema, drawing on Gain-Field neurons from the parietal cortex to align visuo-motor and tactile events across different reference frames. This work has garnered over 30 combined citations, reflecting its influence in cognitive robotics and sensorimotor integration. Tolochko’s research not only advances the engineering of humanoid robots but also offers computational models that inform our understanding of biological perception. Her achievements highlight a rare synthesis of neuroscience principles and practical robotic control, making her a leading voice in the development of socially and physically adept machines.
Research Focus
Key Achievements
Top Papers
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