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In this paper, we revisit the problem of learning a stabilizing controller from a finite number of demonstrations by an expert. By focusing on feedback linearizable systems, we show how to combine expert demonstrations into a stabilizing controller, provided that demonstrations are sufficiently long and there are at least n+1 of them, where n is the number of states of the system being controlled. The results are experimentally demonstrated on a CrazyFlie 2.0 quadrotor.
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It was shown, in recent work by the authors, that it is possible to learn an asymptotically stabilizing controller from a small number of demonstrations performed by an expert on a feedback linearizable system. These results rely on knowledge of the plant dynamics to assemble the learned controller from the demonstrations. In this paper we show how to leverage recent results on data-driven control to dispense with the need to use the plant model. By bringing these two methodologies — learning from demonstrations and data-driven control — together, this paper provides a technique that enables the control of unknown nonlinear feedback linearizable systems solely based on a small number of expert demonstrations.more » « less
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Control algorithms, like model predictive control, can be computationally expensive and may benefit from being executed over the cloud. This is especially the case for nodes at the edge of a network since they tend to have reduced computational capabilities. However, control over the cloud requires transmission of sensitive data (e.g., system dynamics, measurements) which undermines privacy of these nodes. When choosing a method to protect the privacy of these data, efficiency must be considered to the same extent as privacy guarantees to ensure adequate control performance. In this paper, we review a transformation-based method for protecting privacy, previously introduced by the authors, and quantify the level of privacy it provides. Moreover, we also consider the case of adversaries with side knowledge and quantify how much privacy is lost as a function of the side knowledge of the adversarymore » « less
