Josephine Monica

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About

Josephine Monica is a robotics researcher whose work focuses on the intersection of uncertainty quantification, autonomous navigation, and aerial manipulation. Her key research areas include probabilistic modeling for safety-critical systems, vision-based agricultural robotics, and redundancy resolution for unmanned aerial manipulators (UAMs). Monica’s major contributions are threefold: she developed a probabilistic uncertainty quantification framework for neural networks in visual localization, crucial for self-driving cars and other safety-critical applications; she created a vision-based autonomous navigation solution for trellised cropping systems that features automatic annotation, advancing agricultural robotics; and she proposed a multi-task online redundancy resolution strategy for UAMs, enabling complex aerial manipulation tasks by leveraging high degrees of freedom. Her most cited paper (6 citations) addresses the critical challenge of making prediction models trustworthy in robotics, while her agricultural navigation work (4 citations) stands out for its practical approach to field automation. Monica’s research demonstrates a clear trajectory from foundational uncertainty methods to applied systems in both agricultural and aerial robotics, making her a promising voice in the field of autonomous systems.