Papers
2
Total Citations
76
H-Index
2
About
Andrew Stewart is a pioneering researcher in human-robot interaction, specializing in the mathematical modeling of joint decision-making dynamics between humans and autonomous systems. His work bridges cognitive science and robotics, providing foundational frameworks for designing effective human–robot teams. Stewart’s major contributions include developing analytic, model-based descriptions of how humans make decisions in two-alternative choice tasks under social feedback, a methodology that enables systematic design of team and network parameters to optimize performance. His 2011 paper, "Towards Human–Robot Teams," has garnered 41 citations, while his 2008 work on integrating human and robot decision-making dynamics with feedback, cited 35 times, introduced convergence analysis for collaborative tasks where human subjects interact with robots in complex scenarios. By leveraging psychological research on human decision-making, Stewart has advanced the theoretical underpinnings of human–robot collaboration, offering predictive models that inform the design of safer, more efficient autonomous systems. His work is essential reading for researchers in robotics, cognitive engineering, and human factors, establishing him as a key figure in the quest to create truly integrated human–robot teams.
Research Focus
Key Achievements
Top Papers
- 1
- 2