Nicholas Sohre

University of Minnesota, University of Minnesota System

Papers

Total Citations

H-Index

About

Nicholas Sohre’s research lies at the intersection of robotics, human-robot interaction, and computer animation, with a focus on making autonomous systems more intuitive and expressive. His most cited work, “An Augmented Reality Motion Planning Interface for Robotics” (2019, 6 citations), addresses a critical gap in modern robotics: while hardware has advanced rapidly, user interfaces for task-oriented robots have lagged behind. By integrating augmented reality into motion planning, Sohre enables researchers to control robots more naturally, facilitating applications from image capture to sample collection. In “PVL: A Framework for Navigating the Precision-Variety Trade-Off in Automated Animation of Smiles” (2018, 2 citations), he tackles the challenge of generating digital character animations that are both accurate and natural, avoiding the repetitive or unnatural look that plagues many automated systems. His “Multiworld Motion Planning” (2018, 2 citations) introduces a novel approach that plans across multiple predicted outcomes rather than a single scenario, overcoming key limitations in traditional predictive planning. Though his citation counts are modest, Sohre’s work demonstrates a creative ability to bridge robotics and animation, offering practical tools for researchers seeking more flexible, human-centered autonomous systems.