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About

Tesca Fitzgerald is a leading researcher in human-robot interaction and robot learning, whose work focuses on enabling robots to adapt and transfer skills across tasks and environments. Her core contributions lie at the intersection of cognitive robotics and human-guided learning, particularly in developing methods for robots to leverage human feedback to map objects, adapt trajectories, and learn tool affordances. Fitzgerald’s influential work on "Human-Guided Object Mapping for Task Transfer" (22 citations) addresses the fundamental challenge of identifying task-relevant object mappings when transferring learned skills to new environments. Her research on "Metrics for Robot Proficiency Self-assessment and Communication of Proficiency in Human-robot Teams" (21 citations) provides crucial frameworks for evaluating how robots can effectively communicate their capabilities to human teammates. Fitzgerald has also pioneered approaches for creative tool use, as demonstrated in her work on "Human-guided Trajectory Adaptation for Tool Transfer" (18 citations) and "Modeling and Learning Constraints for Creative Tool Use" (11 citations), which explore how robots can improvise with unfamiliar tools through human correction and constraint learning. Her research on "Learning object affordances by leveraging the combination of human-guidance and self-exploration" (18 citations) exemplifies her signature approach of combining human expertise with autonomous robot exploration. Fitzgerald’s work has significant implications for deploying adaptable, collaborative robots in human environments.