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Lang."],"published-print":{"date-parts":[[2024,1,2]]},"abstract":"<jats:p>\n            The goal of\n            <jats:italic toggle=\"yes\">programmatic Learning from Demonstration (LfD)<\/jats:italic>\n            is to learn a policy in a programming language that can be used to control a robot\u2019s behavior from a set of user demonstrations. This paper presents a new programmatic LfD algorithm that targets\n            <jats:italic toggle=\"yes\">long-horizon robot tasks<\/jats:italic>\n            which require synthesizing programs with complex control flow structures, including nested loops with multiple conditionals. Our proposed method first learns a program sketch that captures the target program\u2019s control flow and then completes this sketch using an LLM-guided search procedure that incorporates a novel technique for proving unrealizability of programming-by-demonstration problems. We have implemented our approach in a new tool called\n            <jats:sc>prolex<\/jats:sc>\n            and present the results of a comprehensive experimental evaluation on 120 benchmarks involving complex tasks and environments. We show that, given a 120 second time limit,\n            <jats:sc>prolex<\/jats:sc>\n            can find a program consistent with the demonstrations in 80% of the cases. Furthermore, for 81% of the tasks for which a solution is returned,\n            <jats:sc>prolex<\/jats:sc>\n            is able to find the ground truth program with just one demonstration. In comparison, CVC5, a syntaxguided synthesis tool, is only able to solve 25% of the cases\n            <jats:italic toggle=\"yes\">even when given the ground truth program sketch<\/jats:italic>\n            , and an LLM-based approach, GPT-Synth, is unable to solve any of the tasks due to the environment complexity.\n          <\/jats:p>","DOI":"10.1145\/3632860","type":"journal-article","created":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T20:48:51Z","timestamp":1704487731000},"page":"512-545","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Programming-by-Demonstration for Long-Horizon Robot Tasks"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7028-518X","authenticated-orcid":false,"given":"Noah","family":"Patton","sequence":"first","affiliation":[{"name":"University of Texas, Austin, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9064-0797","authenticated-orcid":false,"given":"Kia","family":"Rahmani","sequence":"additional","affiliation":[{"name":"University of Texas, Austin, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1610-6198","authenticated-orcid":false,"given":"Meghana","family":"Missula","sequence":"additional","affiliation":[{"name":"University of Texas, Austin, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1211-1731","authenticated-orcid":false,"given":"Joydeep","family":"Biswas","sequence":"additional","affiliation":[{"name":"University of Texas, Austin, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8006-1230","authenticated-orcid":false,"given":"I\u015f\u0131l","family":"Dillig","sequence":"additional","affiliation":[{"name":"University of Texas, Austin, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,1,5]]},"reference":[{"key":"e_1_3_1_2_1","unstructured":"Constructions Aeronautiques Adele Howe Craig Knoblock ISI Drew McDermott Ashwin Ram Manuela Veloso Daniel Weld David Wilkins SRI Anthony Barrett Dave Christianson et al. 1998. 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