RHH-LGP: Receding horizon and heuristics-based logic-geometric programming for task and motion planning

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buir.contributor.authorOğuz, Özgür S.
buir.contributor.orcidOğuz, Özgür S.|0000-0001-8723-1837
dc.citation.epage13768en_US
dc.citation.spage13761en_US
dc.contributor.authorBraun, C. V.
dc.contributor.authorOrtiz-Haro, J.
dc.contributor.authorToussaint, M.
dc.contributor.authorOğuz, Özgür S.
dc.coverage.spatialKyoto, Japanen_US
dc.date.accessioned2023-02-24T07:50:41Z
dc.date.available2023-02-24T07:50:41Z
dc.date.issued2022
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionConference Name: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)en_US
dc.descriptionDate of Conference: 23-27 October 2022en_US
dc.description.abstractSequential decision-making and motion planning for robotic manipulation induce combinatorial complexity. For long-horizon tasks, especially when the environment comprises many objects that can be interacted with, planning efficiency becomes even more important. To plan such long-horizon tasks, we present the RHH-LGP algorithm for combined task and motion planning (TAMP). First, we propose a TAMP approach (based on Logic-Geometric Programming) that effectively uses geometry-based heuristics for solving long-horizon manipulation tasks. The efficiency of this planner is then further improved by a receding horizon formulation, resulting in RHH-LGP. We demonstrate the robustness and effectiveness of our approach on a diverse range of long-horizon tasks that require reasoning about interactions with a large number of objects. Using our framework, we can solve tasks that require multiple robots, including a mobile robot and snake-like walking robots, to form novel heterogeneous kinematic structures autonomously. By combining geometry-based heuristics with iterative planning, our approach brings an order-of-magnitude reduction of planning time in all investigated problems.en_US
dc.identifier.doi10.1109/IROS47612.2022.9981797en_US
dc.identifier.eisbn978-1-6654-7927-1
dc.identifier.eissn2153-0866
dc.identifier.isbn978-1-6654-7928-8
dc.identifier.issn2153-0858
dc.identifier.urihttp://hdl.handle.net/11693/111675
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/IROS47612.2022.9981797en_US
dc.source.title2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)en_US
dc.titleRHH-LGP: Receding horizon and heuristics-based logic-geometric programming for task and motion planningen_US
dc.typeConference Paperen_US
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