Deep Neural Networks for Robotics

Deep Neural Networks for Robotics

We aim to develop a platform-independent approach that utilizes deep neural networks (DNNs) to enhance classical controllers to achieve high-performance tracking. In one of our approaches, the DNNs are used as an add-on module approximating the inverse dynamics of a baseline controller to compensate for factors such as, delays or unmodeled dynamics present in the baseline system. As part of this project, based on insights obtained from control theory, we provide guidelines on the selection of DNN inputs and outputs, identify conditions when the proposed approach is effective, and derive a condition to make DNN training more efficient.

 

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University of Toronto Institute for Aerospace Studies