We study robot dynamics, control, and design. We use techniques from the fields of applied mechanics, nonlinear dynamics, optimal control, machine learning, and computational design. Our approach is multidisciplinary and heavily utilizes theoretical and numerical investigations. We aim to develop robots – machines equipped with physical intelligence (adaptability) and control intelligence (decision making capability) – that parallel biological systems.
For detailed insights continue reading at the Braun Lab for Robotics and Control
Highlights
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NSF CAREER Award: Energy-efficient mechanically-adaptive robotics could lead to new-generation industrial robots, transportation systems, and devices that can assist humans |
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NSF (CMMI/DCSD) Award: What if robots could teach robots the same way parents teach their children? |
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Our paper “How to Run 50% Faster without External Energy” was published in Science Advances in March 2020 |
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Our paper “How to Run 50% Faster without External Energy” was publicized in “The Guardian” |
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Our theoretical work on robot exoskeletons is featured in “The Conversation” |