Nathan Hewitt

University of North Carolina at Charlotte

Papers

Total Citations

H-Index

About

Nathan Hewitt is a robotics researcher advancing the frontier of socially intelligent human-robot interaction. His work centers on developing algorithms that allow robots to navigate human environments with genuine social awareness, moving beyond simple collision avoidance to incorporate human intention, preferences, and personal space. Hewitt’s most influential paper, “Socially Aware Robot Obstacle Avoidance Considering Human Intention and Preferences” (22 citations), tackles the critical challenge of making robot motion both physically and psychologically safe for humans. He also led the creation of the Adaptable Platform for Interactive Swarm Robotics (APIS), a pioneering testbed featuring fifty low-cost robots designed to accelerate research in human-swarm interaction (12 citations). Further exploring the nuances of social robotics, his work on the effects of personal space on robot behavior (5 citations) provides empirical, human-in-the-loop assessments that inform the design of more comfortable and acceptable robotic systems. Through these contributions, Hewitt is helping to shape a future where robots are not just tools, but considerate and intuitive partners in shared spaces.