Grant Wang

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About

Grant Wang is a robotics researcher whose work sits at the intersection of machine learning and microrobotics, with a particular focus on data-efficient optimization methods for small-scale robotic systems. His research addresses one of the field's most persistent challenges: designing and controlling robots that lack accurate physical models, such as compliant and micro-scale platforms where traditional engineering approaches often fall short. Wang's most influential contribution, "Data-efficient Learning of Morphology and Controller for a Microrobot" (2019, 44 citations), tackles the costly iterative process of robot design by simultaneously optimizing both physical morphology and control strategies using minimal real-world data. Complementing this, his 2018 paper "Learning Flexible and Reusable Locomotion Primitives for a Microrobot" (30 citations) introduced data-driven gait optimization techniques that eliminate the need for expert-designed locomotion behaviors, making autonomous gait learning practical even without reliable physical models. Collectively, Wang's research demonstrates a consistent drive to reduce the data and human expertise required to develop capable microrobots. His work has meaningfully advanced the field of autonomous robot design and has garnered growing attention from the broader robotics and machine learning communities.