TY - JOUR

T1 - Non-smooth Newton methods for deformable multi-body dynamics

AU - Macklin, Miles

AU - Erleben, Kenny

AU - Müller, Matthias

AU - Chentanez, Nuttapong

AU - Jeschke, Stefan

AU - Makoviychuk, Viktor

PY - 2019

Y1 - 2019

N2 - We present a framework for the simulation of rigid and deformable bodies in the presence of contact and friction. Our method is based on a nonsmooth Newton iteration that solves the underlying nonlinear complementarity problems (NCPs) directly. This approach allows us to support nonlinear dynamics models, including hyperelastic deformable bodies and articulated rigid mechanisms, coupled through a smooth isotropic friction model. The fixed-point nature of our method means it requires only the solution of a symmetric linear system as a building block. We propose a new complementarity preconditioner for NCP functions that improves convergence, and we develop an efficient GPU-based solver based on the conjugate residual (CR) method that is suitable for interactive simulations. We show how to improve robustness using a new geometric stiffness approximation and evaluate our method's performance on a number of robotics simulation scenarios, including dexterous manipulation and training using reinforcement learning.

AB - We present a framework for the simulation of rigid and deformable bodies in the presence of contact and friction. Our method is based on a nonsmooth Newton iteration that solves the underlying nonlinear complementarity problems (NCPs) directly. This approach allows us to support nonlinear dynamics models, including hyperelastic deformable bodies and articulated rigid mechanisms, coupled through a smooth isotropic friction model. The fixed-point nature of our method means it requires only the solution of a symmetric linear system as a building block. We propose a new complementarity preconditioner for NCP functions that improves convergence, and we develop an efficient GPU-based solver based on the conjugate residual (CR) method that is suitable for interactive simulations. We show how to improve robustness using a new geometric stiffness approximation and evaluate our method's performance on a number of robotics simulation scenarios, including dexterous manipulation and training using reinforcement learning.

KW - Contact

KW - Friction

KW - Multi-body dynamics

KW - Numerical optimization

KW - Robotics

U2 - 10.1145/3338695

DO - 10.1145/3338695

M3 - Journal article

AN - SCOPUS:85074424851

VL - 38

JO - ACM Transactions on Graphics

JF - ACM Transactions on Graphics

SN - 0730-0301

IS - 5

M1 - 140

ER -