Andrew McManus

Papers

Total Citations

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About

Andrew McManus is a researcher whose work lies at the intersection of robotics, computer vision, and spatial reasoning. His primary focus is on enabling robots to understand and interact with their environments by learning spatial relationships from 3D visual data. McManus’s most cited work, "Learning spatial relationships from 3D vision using histograms" (2014, 18 citations), introduces a novel method for extracting features that determine what manipulation actions are possible—a concept rooted in the theory of affordances. This contribution is pivotal for advancing autonomous robotic manipulation, as it moves beyond simple object recognition to a deeper understanding of functional interactions. By leveraging 3D histograms, McManus’s approach allows robots to infer not just what objects are present, but what can be done with them, bridging the gap between perception and action. His research has significant implications for fields such as service robotics, manufacturing, and assistive technologies, where robots must operate in unstructured human environments. McManus’s work represents a thoughtful step toward more intuitive and capable robotic systems, making him a notable voice in the ongoing effort to create machines that perceive and act with human-like spatial intelligence.