David Kames

Papers

Total Citations

H-Index

About

David Kames is a researcher at the forefront of human-robot collaboration, with a particular focus on the dynamics of flexible task allocation in human-cobot teams. His most-cited work, "Learning to Share - Teaching the Impact of Flexible Task Allocation in Human-cobot Teams" (2021), has garnered 12 citations and represents a key contribution to understanding how cobots can adaptively share responsibilities with human partners. Kames’ research explores the intersection of cognitive science and robotics, aiming to design systems that not only perform tasks but also learn from and teach human teammates to optimize joint workflows. His work addresses critical challenges in trust, efficiency, and communication within hybrid teams, offering insights that bridge theoretical frameworks and practical applications in manufacturing and service robotics. By emphasizing the educational aspect of human-robot interaction, Kames has helped shape how researchers and practitioners approach the design of collaborative systems that are both intuitive and effective. His contributions are particularly valuable for students and researchers interested in the future of work, where seamless human-robot cooperation is essential.