<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Conference Paper</dc:product_type><dc:title>Watch and Learn: Learning to control feedback linearizable systems from expert demonstrations</dc:title><dc:creator>Sultangazin, Alimzhan; Pannocchi, Luigi; Fraile, Lucas; Tabuada, Paulo</dc:creator><dc:corporate_author/><dc:editor/><dc:description>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.</dc:description><dc:publisher/><dc:date>2022-05-23</dc:date><dc:nsf_par_id>10411915</dc:nsf_par_id><dc:journal_name>2022 International Conference on Robotics and Automation (ICRA)</dc:journal_name><dc:journal_volume/><dc:journal_issue/><dc:page_range_or_elocation>8577 to 8583</dc:page_range_or_elocation><dc:issn/><dc:isbn/><dc:doi>https://doi.org/10.1109/ICRA46639.2022.9812054</dc:doi><dcq:identifierAwardId>1705135</dcq:identifierAwardId><dc:subject/><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>