Marie D. Manner

University of Minnesota, University of Minnesota System

Papers

Total Citations

244

H-Index

About

Marie D. Manner is a pioneering researcher at the intersection of robotics, artificial intelligence, and developmental psychology. Her work primarily explores how autonomous systems can be designed to interact with and understand human behavior, with a particular focus on child-robot interaction and complex task allocation. Her most influential contribution, "A taxonomy for task allocation problems with temporal and ordering constraints" (2016), has garnered 235 citations and provides a foundational framework for multi-agent systems, enabling robots to coordinate efficiently under real-world scheduling demands. Manner is also recognized for her innovative work in early autism detection; her 2015 study using the small humanoid robot NAO to elicit social cues in toddlers represents a novel, non-invasive diagnostic tool, despite its niche citation count. Additionally, her 2018 paper on graphically representing child-robot interaction proxemics offers valuable insights into how spatial behavior can inform robot design for therapeutic and educational settings. Manner’s research bridges engineering and clinical application, demonstrating how robotics can both advance AI coordination and serve vulnerable populations. Her work continues to inspire interdisciplinary approaches to human-robot collaboration.