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About

M. McTaggart is a roboticist whose work centers on autonomous manipulation, robotic perception, and cost-effective hardware design for unstructured environments. McTaggart’s most significant contribution is leading the design of Cartman, a low-cost Cartesian manipulator that won first place in the 2017 Amazon Robotics Challenge. This system, detailed in a highly cited 2018 paper (141 citations), demonstrated that affordable, custom-built robots could outperform more expensive alternatives in complex pick-and-place tasks. A key innovation was the integrated design of a multi-modal end-effector and grasping system, which enabled reliable handling of small, shiny, and transparent objects. McTaggart also advanced robotic perception by developing semantic segmentation techniques that function effectively with limited training data, a critical capability for handling unseen object categories in competition settings. This work, published in 2018 (52 citations), directly contributed to the team’s victory. By proving that thoughtful mechanical design and efficient perception algorithms can overcome the challenges of cluttered, real-world environments, McTaggart’s research has had a lasting impact on warehouse automation and agricultural robotics.