DC FieldValueLanguage
dc.contributor.authorAgboh, Wisdom-
dc.contributor.authorRuprecht, Daniel-
dc.contributor.authorDogar, Mehmet R.-
dc.date.accessioned2022-03-21T10:52:55Z-
dc.date.available2022-03-21T10:52:55Z-
dc.date.issued2019-10-
dc.identifier.citation17th International Symposium of Robotics Research (ISRR 2019)de_DE
dc.identifier.isbn978-303095458-1de_DE
dc.identifier.issn2511-1256de_DE
dc.identifier.urihttp://hdl.handle.net/11420/12045-
dc.description.abstractWe present a method for fast and accurate physics-based predictions during non-prehensile manipulation planning and control. Given an initial state and a sequence of controls, the problem of predicting the resulting sequence of states is a key component of a variety of model-based planning and control algorithms. We propose combining a coarse (i.e. computationally cheap but not very accurate) predictive physics model, with a fine (i.e. computationally expensive but accurate) predictive physics model, to generate a hybrid model that is at the required speed and accuracy for a given manipulation task. Our approach is based on the Parareal algorithm, a parallel-in-time integration method used for computing numerical solutions for general systems of ordinary differential equations. We adapt Parareal to combine a coarse pushing model with an off-the-shelf physics engine to deliver physics-based predictions that are as accurate as the physics engine but run in substantially less wall-clock time, thanks to parallelization across time. We use these physics-based predictions in a model-predictive-control framework based on trajectory optimization, to plan pushing actions that avoid an obstacle and reach a goal location. We show that with hybrid physics models, we can achieve the same success rates as the planner that uses the off-the-shelf physics engine directly, but significantly faster. We present experiments in simulation and on a real robotic setup. Videos are available here: https://youtu.be/5e9oTeu4JOU.en
dc.language.isoende_DE
dc.relation.ispartofSpringer proceedings in advanced roboticsde_DE
dc.subjectModel-predictive-controlde_DE
dc.subjectPhysics-based manipulationde_DE
dc.titleCombining Coarse and Fine Physics for Manipulation Using Parallel-in-Time Integrationde_DE
dc.typeinProceedingsde_DE
dc.type.dinicontributionToPeriodical-
dcterms.DCMITypeText-
tuhh.abstract.englishWe present a method for fast and accurate physics-based predictions during non-prehensile manipulation planning and control. Given an initial state and a sequence of controls, the problem of predicting the resulting sequence of states is a key component of a variety of model-based planning and control algorithms. We propose combining a coarse (i.e. computationally cheap but not very accurate) predictive physics model, with a fine (i.e. computationally expensive but accurate) predictive physics model, to generate a hybrid model that is at the required speed and accuracy for a given manipulation task. Our approach is based on the Parareal algorithm, a parallel-in-time integration method used for computing numerical solutions for general systems of ordinary differential equations. We adapt Parareal to combine a coarse pushing model with an off-the-shelf physics engine to deliver physics-based predictions that are as accurate as the physics engine but run in substantially less wall-clock time, thanks to parallelization across time. We use these physics-based predictions in a model-predictive-control framework based on trajectory optimization, to plan pushing actions that avoid an obstacle and reach a goal location. We show that with hybrid physics models, we can achieve the same success rates as the planner that uses the off-the-shelf physics engine directly, but significantly faster. We present experiments in simulation and on a real robotic setup. Videos are available here: https://youtu.be/5e9oTeu4JOU.de_DE
tuhh.publisher.doi10.1007/978-3-030-95459-8_44-
tuhh.publication.instituteMathematik E-10de_DE
tuhh.type.opusInProceedings (Aufsatz / Paper einer Konferenz etc.)-
dc.type.drivercontributionToPeriodical-
dc.type.casraiConference Paper-
tuhh.container.volume20 SPARde_DE
tuhh.container.startpage725de_DE
tuhh.container.endpage740de_DE
dc.relation.conference17th International Symposium of Robotics Research, ISRR 2019de_DE
dc.identifier.scopus2-s2.0-85126228543-
datacite.resourceTypeConference Paper-
datacite.resourceTypeGeneralText-
item.languageiso639-1en-
item.grantfulltextnone-
item.creatorOrcidAgboh, Wisdom-
item.creatorOrcidRuprecht, Daniel-
item.creatorOrcidDogar, Mehmet R.-
item.mappedtypeinProceedings-
item.creatorGNDAgboh, Wisdom-
item.creatorGNDRuprecht, Daniel-
item.creatorGNDDogar, Mehmet R.-
item.fulltextNo Fulltext-
item.openairetypeinProceedings-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.cerifentitytypePublications-
crisitem.author.deptMathematik E-10-
crisitem.author.orcid0000-0002-0242-0215-
crisitem.author.orcid0000-0003-1904-2473-
crisitem.author.orcid0000-0002-6896-5461-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
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