{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-heuristic-search-via-imitation","title":"Learning Heuristic Search via Imitation","arxiv_id":"1707.03034","date":"2017-07-10","proceeding":null,"authors":["Mohak Bhardwaj","Sanjiban Choudhury","Sebastian Scherer"],"abstract":"Robotic motion planning problems are typically solved by constructing a\nsearch tree of valid maneuvers from a start to a goal configuration. Limited\nonboard computation and real-time planning constraints impose a limit on how\nlarge this search tree can grow. Heuristics play a crucial role in such\nsituations by guiding the search towards potentially good directions and\nconsequently minimizing search effort. Moreover, it must infer such directions\nin an efficient manner using only the information uncovered by the search up\nuntil that time. However, state of the art methods do not address the problem\nof computing a heuristic that explicitly minimizes search effort. In this\npaper, we do so by training a heuristic policy that maps the partial\ninformation from the search to decide which node of the search tree to expand.\nUnfortunately, naively training such policies leads to slow convergence and\npoor local minima. We present SaIL, an efficient algorithm that trains\nheuristic policies by imitating \"clairvoyant oracles\" - oracles that have full\ninformation about the world and demonstrate decisions that minimize search\neffort. We leverage the fact that such oracles can be efficiently computed\nusing dynamic programming and derive performance guarantees for the learnt\nheuristic. We validate the approach on a spectrum of environments which show\nthat SaIL consistently outperforms state of the art algorithms. Our approach\npaves the way forward for learning heuristics that demonstrate an anytime\nnature - finding feasible solutions quickly and incrementally refining it over\ntime.","url_abs":"http://arxiv.org/abs/1707.03034v1","url_pdf":"http://arxiv.org/pdf/1707.03034v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-heuristic-search-via-imitation","repo_url":"https://github.com/omron-sinicx/planning-datasets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"heuristic-search","task_name":"Heuristic Search"},{"task_slug":"motion-planning","task_name":"Motion Planning"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.03034","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}