Publication | Closed Access
Primal/Dual Descent Methods for Dynamics
36
Citations
50
References
2020
Year
Numerical AnalysisRobot KinematicsEngineeringProjective DynamicsDexterous ManipulationMechanical EngineeringConstrained OptimizationComputational MechanicsMechanics ModelingMechanicsContact MechanicDerivative-free OptimizationKinematicsDeformation ModelingContinuous OptimizationPrimal/dual Descent MethodsContact Force DistributionsConvex OptimizationMechanical SystemsRobotic ManipulationRobotics
Abstract We 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.
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