Shane T. Mueller
Papers
3
Total Citations
4
H-Index
1
About
Shane T. Mueller’s research lies at the intersection of human-robot interaction, cognitive engineering, and adaptive training systems, with a focus on how humans and autonomous agents collaborate effectively. His major contributions include developing frameworks to evaluate path planning in human-robot teams, where he quantified path agreement and mental model congruency to improve team coordination. This work, published in 2017, has garnered 2 citations and laid groundwork for understanding shared cognition in collaborative robotics. More recently, Mueller has explored the cognitive impacts of human-robot collaboration in modular construction, addressing overlooked human factors such as workload and situation awareness in industrial settings. His 2025 paper on this topic (1 citation) highlights the need for worker-centered design in construction automation. Additionally, Mueller is pioneering the use of large language models and knowledge space representations to create adaptive cybertraining tools for construction education, integrating worked examples to personalize learning. His research bridges technical robotics with human cognition, offering practical insights for safer, more efficient human-robot teams in high-stakes environments like construction.
Research Focus
Key Achievements
Top Papers
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