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dc.contributor.authorVega-Brown, William R
dc.contributor.authorRoy, Nicholas
dc.date.accessioned2021-05-11T18:14:39Z
dc.date.available2021-05-11T18:14:39Z
dc.date.issued2020-04
dc.identifier.issn2374-3468
dc.identifier.issn2159-5399
dc.identifier.urihttps://hdl.handle.net/1721.1/130570
dc.description.abstractWe present a new representation for task and motion planning that uses constraints to capture both continuous and discrete phenomena in a unified framework. We show that we can decide if a feasible plan exists for a given problem instance using only polynomial space if the constraints are semialgebraic and all actions have uniform stratified accessibility, a technical condition closely related to both controllability and to the existence of a symbolic representation of a planning domain. We show that there cannot exist an algorithm that solves the more general problem of deciding if a plan exists for an instance with arbitrary semialgebraic constraints. Finally, we show that our formalism is universal, in the sense that every deterministic robotic planning problem can be well-approximated within our formalism. Together, these results imply task and motion planning is PSPACE-complete.en_US
dc.publisherAssociation for the Advancement of Artificial Intelligence (AAAI)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1609/aaai.v34i06.6607en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceProf. Royen_US
dc.titleTask and Motion Planning Is PSPACE-Completeen_US
dc.typeArticleen_US
dc.identifier.citationVega-Brown, William and Nicholas Roy. "Task and Motion Planning Is PSPACE-Complete." Proceedings of the AAAI Conference on Artificial Intelligence 34, 6 (April 2020): 10385-10392. © 2020 Association for the Advancement of Artificial Intelligenceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.approverRoy, Nicholasen_US
dc.relation.journalProceedings of the AAAI Conference on Artificial Intelligenceen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.date.submission2021-05-07T17:21:49Z
mit.journal.volume34en_US
mit.journal.issue6en_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusComplete


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