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About

Nina Moorman is a robotics researcher whose work sits at the intersection of human-robot interaction, imitation learning, and assistive robotics. Her research focuses on making robots more accessible to non-experts through Learning from Demonstration (LfD), with a particular emphasis on the challenges of task and motion planning with hierarchical abstractions. Moorman's most impactful work, "MIND MELD" (23 citations), introduces personalized meta-learning for robot-centric imitation learning, addressing the performance gap between human- and robot-centric approaches. Her 2021 paper "LanCon-Learn" (20 citations) advances multi-task manipulation by leveraging language to enable generalization across tasks. Notably, Moorman has also explored the intersection of robotics and athletics, developing an athletic mobile manipulator system for robotic wheelchair tennis (2023, 16 citations), demonstrating her commitment to socially impactful applications. Her research extends beyond technical contributions to examine the human factors of robotics, including how user experience impacts hierarchical abstraction learning and how social dynamics affect collaborative robot adoption. With over 97 total citations across her publications, Moorman is establishing herself as a thoughtful voice in making robotics more intuitive, accessible, and socially integrated.