Daniel Rammer
Papers
1
Total Citations
10
H-Index
1
About
Daniel Rammer is a pioneering researcher at the intersection of human-robot interaction (HRI) and mixed reality, with a core focus on trust dynamics, self-efficacy, and collaborative cognition in human-robot teams. His most cited work, "Teaming with a Robot in Mixed Reality: Dynamics of Trust, Self-Efficacy, and Mental Models Affected by Information Richness" (2024, 10 citations), introduces a novel mixed-reality game paradigm to systematically examine how varying levels of introductory information shape users’ mental models, trust calibration, and willingness to collaborate with mobile robots. By demonstrating that information richness significantly influences both initial trust and ongoing self-efficacy during joint tasks, Rammer’s research provides actionable insights for designing more intuitive and trustworthy robotic teammates. His work bridges cognitive science and engineering, offering a framework for understanding how humans form and update mental models of autonomous agents in immersive environments. Rammer’s contributions are particularly relevant for applications in manufacturing, healthcare, and education, where effective human-robot collaboration is critical. His innovative use of mixed reality as a controlled experimental platform sets a new standard for studying real-time trust dynamics, making him a rising voice in the future of collaborative robotics.
Research Focus
Key Achievements
Top Papers
- 1