Robust Controller Design

We have proposed a generalized control policy for rapid learning and control of unknown dynamics such as human-robot interaction or unmodeled disturbance, using Gaussian Process Regression (GPR). GPR provides granular control for the tradeoff between bias and variance which is very critical in hyperparameter tuning and also influence the speed of training. We suggest that GPR could be an alternative to the present global policy optimizers such as neural network, solidifying the usage in environment independent control of interactive controllers.

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