Primal/Dual Descent Methods for Dynamics

dc.contributor.authorMacklin, Milesen_US
dc.contributor.authorErleben, Kennyen_US
dc.contributor.authorMüller, Matthiasen_US
dc.contributor.authorChentanez, Nuttapongen_US
dc.contributor.authorJeschke, Stefanen_US
dc.contributor.authorKim, Tae-Yongen_US
dc.contributor.editorBender, Jan and Popa, Tiberiuen_US
dc.date.accessioned2020-10-16T06:25:10Z
dc.date.available2020-10-16T06:25:10Z
dc.date.issued2020
dc.description.abstractWe examine the relationship between primal, or force-based, and dual, or constraint-based formulations of dynamics. Variational frameworks such as Projective Dynamics have proved popular for deformable simulation, however they have not been adopted for contact-rich scenarios such as rigid body simulation. We propose a new preconditioned frictional contact solver that is compatible with existing primal optimization methods, and competitive with complementarity-based approaches. Our relaxed primal model generates improved contact force distributions when compared to dual methods, and has the advantage of being differentiable, making it well-suited for trajectory optimization. We derive both primal and dual methods from a common variational point of view, and present a comprehensive numerical analysis of both methods with respect to conditioning. We demonstrate our method on scenarios including rigid body contact, deformable simulation, and robotic manipulation.en_US
dc.description.number8
dc.description.sectionheadersRigid Bodies
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume39
dc.identifier.doi10.1111/cgf.14104
dc.identifier.issn1467-8659
dc.identifier.pages89-100
dc.identifier.urihttps://doi.org/10.1111/cgf.14104
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14104
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectSimulation by animation
dc.subjectInteractive simulation
dc.subjectComputer systems organization
dc.subjectRobotics
dc.titlePrimal/Dual Descent Methods for Dynamicsen_US
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