Variations of Augmented Lagrangian for Robotic Multi-Contact Simulation

TL;DR

This paper introduces two novel Augmented Lagrangian-based solvers, CANAL and SubADMM, for efficient and accurate multi-contact robotic simulations, addressing challenges of performance and scalability.

Contribution

The paper adapts Augmented Lagrangian methods to multi-contact NCPs in robotics, proposing two tailored algorithms with improved accuracy and computational efficiency.

Findings

CANAL provides accurate, robust solutions for multi-contact NCPs.

SubADMM offers faster computation and better scalability for high-DOF systems.

Proposed methods outperform existing solvers in robotic manipulation scenarios.

Abstract

The multi-contact nonlinear complementarity problem (NCP) is a naturally arising challenge in robotic simulations. Achieving high performance in terms of both accuracy and efficiency remains a significant challenge, particularly in scenarios involving intensive contacts and stiff interactions. In this article, we introduce a new class of multi-contact NCP solvers based on the theory of the Augmented Lagrangian (AL). We detail how the standard derivation of AL in convex optimization can be adapted to handle multi-contact NCP through the iteration of surrogate problem solutions and the subsequent update of primal-dual variables. Specifically, we present two tailored variations of AL for robotic simulations: the Cascaded Newton-based Augmented Lagrangian (CANAL) and the Subsystem-based Alternating Direction Method of Multipliers (SubADMM). We demonstrate how CANAL can manage multi-contact NCP in an accurate and robust manner, while SubADMM offers superior computational speed, scalability, and parallelizability for high degrees-of-freedom multibody systems with numerous contacts. Our results showcase the effectiveness of the proposed solver framework, illustrating its advantages in various robotic manipulation scenarios.