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About

Miguel Arduengo is a leading researcher at the intersection of robotics, human-robot interaction, and machine learning. His work focuses on enabling robots to learn and replicate complex human motions, with key contributions in whole-body motion transfer, person-following architectures, and Gaussian-process-based learning from demonstration. His most cited paper, “Human to robot whole-body motion transfer” (2021, 18 citations), presents a framework for teleoperating mobile manipulators by mapping human movements to robot actions, a critical step toward safe physical collaboration in shared environments. Another highly cited work, “An Architecture for Person-Following using Active Target Search” (2018, 18 citations), introduces a real-time system for mobile robots to detect, track, and follow humans, enhancing autonomous navigation in dynamic settings. Arduengo’s research on Gaussian-process-based learning from demonstration (2023, 17 citations) allows robots to encode task constraints from human demonstrations, advancing skill transfer for manipulation tasks. He has also explored the societal implications of automation in “The Robot Economy: Here It Comes” (2020, 14 citations). With over 75 total citations, Arduengo’s work is shaping the future of intuitive, human-aware robotics.