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We present a new technique to enhance the robustness of imitation learning methods by generating corrective data to account for compounding errors and disturbances.
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Dean A Pomerleau · 1988
Earlier work this paper cites.
Adaptive control of nonlinear systems with a triangular structure
Danbing Seto, Anuradha M Annaswamy, and John Baillieul · 1994
Earlier work this paper cites.
Support vector machines
Marti A. Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf · 1998
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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