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About

Sinan Kalkan is a prominent robotics and computer vision researcher whose work sits at the intersection of robot learning, cognitive development, and human-robot interaction. His research has made significant contributions to how robots perceive, understand, and interact with their environments and the people within them. Kalkan's early foundational work explored how robots actively discover and segment objects through exploratory behavior — a process he framed as the "birth of the object" — earning over 80 citations and establishing him as a key voice in developmental robotics. He has been particularly influential in grounding language in robot sensorimotor experience, demonstrating how humanoid robots like iCub can learn noun, adjective, and verb concepts directly from affordance-based interactions, collectively drawing nearly 150 citations across related publications. His more recent work reflects a sharp turn toward long-term human-robot interaction, where he has championed continual and lifelong learning as essential for robots adapting to human affective states, social norms, and individual preferences over time. His contributions to the LEAP-HRI initiative and datasets for assessing social appropriateness of robot actions underscore a growing concern for robots that remain relevant, personalized, and socially responsible across extended deployments. With over 375 cumulative citations, Kalkan's portfolio represents a coherent vision of robots as genuine cognitive and social agents.