Journal ArticleDOI
Learning to control from expert demonstrations
TLDR
This paper shows 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.Abstract:
In this paper, we revisit the problem of learning a stabilizing controller from a finite number of demonstrations by an expert. By first 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. When we have more than n+ 1 demonstrations, we discuss how to optimally choose the best n + 1 demonstrations to construct the stabilizing controller. We then extend these results to a class of systems that can be embedded into a higher-dimensional system containing a chain of integrators. The feasibility of the proposed algorithm is demonstrated by applying it on a CrazyFlie 2.0 quadrotor.read more
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