Max Robotics — Global Service Robot Directory + US Certification

About

Matthew Field is a leading researcher in the intersection of human motion analysis and robotic systems, with a career dedicated to bridging the gap between human movement and machine learning. His foundational work includes two highly influential surveys—"Motion capture in robotics review" (2009, 89 citations) and "Human motion capture sensors and analysis in robotics" (2011, 55 citations)—which systematically evaluate sensor technologies and data processing methods, establishing a critical roadmap for integrating motion capture into robotics. Field’s major contributions extend to robot programming by demonstration, where his 2015 paper on "Learning Trajectories for Robot Programing by Demonstration Using a Coordinated Mixture of Factor Analyzers" (40 citations) introduces a robust framework for modeling joint-space trajectories through hidden Markov models and nonlinear dynamical systems, enabling robots to learn complex motions from human examples. He has also advanced teleoperation with his work on "Nonlinear bilateral teleoperation using extended active observer for force estimation and disturbance suppression" (2014), proposing an innovative algorithm for simultaneous force estimation and disturbance rejection. With over 190 citations across his key publications, Field’s research has profoundly shaped how robots perceive, learn from, and interact with human motion, making him a pivotal figure in modern robotics and human-robot interaction.