{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T12:04:13Z","timestamp":1753272253475},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>We define an admissibility condition for abstractions expressed using angelic semantics and show that these conditions allow us to accelerate planning while preserving the ability to find the optimal motion plan.\u00a0 We then derive admissible abstractions for two motion planning domains with continuous state.\u00a0 We extract upper and lower bounds on the cost of concrete motion plans using local metric and topological properties of the problem domain.\u00a0 These bounds guide the search for a plan while maintaining performance guarantees.\u00a0 We show that abstraction can dramatically reduce the complexity of search relative to a direct motion planner.\u00a0 Using our abstractions, we find near-optimal motion plans in planning problems involving 10^13\u00a0states without using a separate task planner.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/674","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"4852-4859","source":"Crossref","is-referenced-by-count":16,"title":["Admissible Abstractions for Near-optimal Task and Motion Planning"],"prefix":"10.24963","author":[{"given":"William","family":"Vega-Brown","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas","family":"Roy","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2018","name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","start":{"date-parts":[[2018,7,13]]},"theme":"Artificial Intelligence","location":"Stockholm, Sweden","end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:55:10Z","timestamp":1530770110000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/674"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/674","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}