乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T22:10:23Z","timestamp":1729030223162},"reference-count":51,"publisher":"SAGE Publications","issue":"4-5","license":[{"start":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T00:00:00Z","timestamp":1715817600000},"content-version":"vor","delay-in-days":411,"URL":"http:\/\/www.sagepub.com\/licence-information-for-chorus"}],"funder":[{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-17-1-2699"],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2023,4]]},"abstract":" We present a framework for the automatic encoding and repair of high-level tasks. Given a set of skills a robot can perform, our approach first abstracts sensor data into symbols and then automatically encodes the robot\u2019s capabilities in Linear Temporal Logic (LTL). Using this encoding, a user can specify reactive high-level tasks, for which we can automatically synthesize a strategy that executes on the robot, if the task is feasible. If a task is not feasible given the robot\u2019s capabilities, we present two methods, one enumeration-based and one synthesis-based, for automatically suggesting additional skills for the robot or modifications to existing skills that would make the task feasible. We demonstrate our framework on a Baxter robot manipulating blocks on a table, a Baxter robot manipulating plates on a table, and a Kinova arm manipulating vials, with multiple sensor modalities, including raw images. <\/jats:p>","DOI":"10.1177\/02783649231167207","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T20:31:15Z","timestamp":1684269075000},"page":"263-288","update-policy":"http:\/\/dx.doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic encoding and repair of reactive high-level tasks with learned abstract representations"],"prefix":"10.1177","volume":"42","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-4349-1888","authenticated-orcid":false,"given":"Adam","family":"Pacheck","sequence":"first","affiliation":[{"name":"Cornell University, Ithaca, NY, USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4366-4125","authenticated-orcid":false,"given":"Steven","family":"James","sequence":"additional","affiliation":[{"name":"University of the Witwatersrand, Johannesburg, South Africa"}]},{"given":"George","family":"Konidaris","sequence":"additional","affiliation":[{"name":"Brown University, Providence, RI, USA"}]},{"given":"Hadas","family":"Kress-Gazit","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, NY, USA"}]}],"member":"179","published-online":{"date-parts":[[2023,5,16]]},"reference":[{"key":"bibr1-02783649231167207","unstructured":"Ahmetoglu A, Seker M, Sayin A, et al. 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