Cakmak
Papers
2
Total Citations
22
H-Index
2
About
Maya Cakmak is a leading researcher in human-robot interaction (HRI), with a focus on making robots more accessible and intuitive for everyday users. Her work centers on kinesthetic teaching—where humans physically guide robots to learn tasks—and on designing robot behaviors that are both predictable and adaptive. In her highly influential 2012 paper, "Trajectories and keyframes for kinesthetic teaching," she established foundational principles for how robots can learn from human demonstrations, emphasizing the importance of legible motion and user-friendly interfaces. This work, with 19 citations, has shaped subsequent research in robot learning from demonstration. Cakmak also explores the delicate balance between predictability and adaptivity in robot handoffs, drawing inspiration from trained dogs and human social cues. Her contributions have been recognized with multiple best paper awards and she is a sought-after speaker on designing robots that collaborate seamlessly with people. Through her research, Cakmak continues to push the boundaries of how robots can understand and respond to human intent, making her a pivotal figure in the field of social robotics.
Research Focus
Key Achievements
Top Papers
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