<?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>Stronger Generalization Guarantees for Robot Learning by Combining Generative Models and Real-World Data</dc:title><dc:creator>Agarwal, Abhinav; Veer, Sushant; Ren, Allen Z.; Majumdar, Anirudha</dc:creator><dc:corporate_author/><dc:editor/><dc:description/><dc:publisher/><dc:date>2022-01-01</dc:date><dc:nsf_par_id>10338266</dc:nsf_par_id><dc:journal_name>IEEE International Conference on Robotics and Automation</dc:journal_name><dc:journal_volume/><dc:journal_issue/><dc:page_range_or_elocation/><dc:issn/><dc:isbn/><dc:doi>https://doi.org/10.1109/ICRA46639.2022.9811565</dc:doi><dcq:identifierAwardId>2044149</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>