Lauren Reinerman-Jones
Papers
19
Total Citations
294
H-Index
8
About
Lauren Reinerman-Jones is a leading researcher at the intersection of human performance, robotics, and artificial intelligence, with a focus on human-robot teaming and adaptive automation. Her most influential work, "Evolution and revolution: Personality research for the coming world of robots, artificial intelligence, and autonomous systems" (114 citations), explores how human personality traits shape interactions with autonomous systems, laying groundwork for designing more intuitive AI. She has made significant contributions to understanding operator workload and situation awareness, particularly in high-stakes domains like nuclear operations and military reconnaissance, as seen in her 2018 paper on human performance metrics (30 citations). Reinerman-Jones pioneered tactile communication for human-robot interaction, developing a standardized "tactile language" using tactons for robot-to-human messaging (23 citations), and has investigated closed-loop systems that adapt robot behaviors based on physiological measures of operator state (24 citations). Her work on adaptive automation, including studies on unmanned ground vehicle operations (20 citations), demonstrates how autonomy can be dynamically adjusted to optimize performance and reduce cognitive load. Reinerman-Jones’s research bridges theory and application, advancing safer, more effective human-machine collaboration across defense, nuclear, and robotics sectors.
Research Focus
Key Achievements
Top Papers
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- 3Enhancing the effectiveness of human-robot teaming with a closed-loop system24 citations · 2017
- 4Toward a Tactile Language for Human–Robot Interaction23 citations · 2014
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- 6Visual and tactile interfaces for bi-directional human robot communication15 citations · 2013
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- 9The Relevance of Theory to Human-Robot Teaming Research and Development8 citations · 2016
- 10