Thomaz
Papers
2
Total Citations
29
H-Index
2
About
A pioneer in human-robot interaction, Thomaz explores how robots can learn from and connect with people through intuitive, physical means. Their foundational work on kinesthetic teaching—where humans physically guide a robot’s movements—introduced the concept of using trajectories and keyframes to capture and transfer skills, a paradigm that has shaped modern robot learning from demonstration. This research, cited over 19 times, emphasizes the importance of designing interactions that feel natural and collaborative. Equally impactful is Thomaz’s investigation into robot-initiated touch, a study that uncovered how people subjectively respond when a robot reaches out and makes contact. With 10 citations, this work challenges assumptions about human-robot boundaries and opens new avenues for affective robotics. By blending engineering rigor with insights from psychology and design, Thomaz has laid a critical foundation for robots that are not just tools, but socially aware partners. Their contributions continue to inspire researchers seeking to build machines that learn from, and connect with, humans in deeply intuitive ways.
Research Focus
Key Achievements
Top Papers
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