Ezgi Kayhan
Papers
1
Total Citations
22
H-Index
1
About
Ezgi Kayhan is a leading researcher at the intersection of developmental robotics, cognitive science, and artificial intelligence, with a core focus on how robots can develop a sense of self through sensorimotor interaction. Her most cited work, the 2021 survey "Sensorimotor Representation Learning for an 'Active Self' in Robots," has garnered 22 citations and provides a foundational framework for enabling safe human-robot collaboration. Kayhan’s major contribution lies in modeling how robots can learn an "active self" — the ability to understand their own body and actions in dynamic, unstructured environments — rather than relying on pre-programmed rules. This approach is critical for creating robots that can adapt to human spaces intuitively and safely. Her research bridges insights from infant development and machine learning, offering a biologically inspired pathway to more autonomous and socially aware machines. By tackling the challenge of self-representation in artificial agents, Kayhan’s work is shaping the next generation of robots capable of learning from experience, with profound implications for assistive technology, manufacturing, and everyday human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1