Quasistatic contact-rich manipulation via linear complementarity quadratic programming

TL;DR

This paper introduces a real-time control framework for contact-rich manipulation that models quasistatic dynamics with complementarity constraints and uses a linear complementarity quadratic program to determine control actions adaptively.

Contribution

It presents a novel LCQP-based approach that efficiently handles contact mode decisions and relaxes constraints to improve robustness in contact-rich manipulation tasks.

Findings

Successfully performs contact-rich tasks in simulations

Determines contact modes and control actions in real-time

Handles model noise and measurement uncertainties effectively

Abstract

Contact-rich manipulation is challenging due to dynamically-changing physical constraints by the contact mode changes undergone during manipulation. This paper proposes a versatile local planning and control framework for contact-rich manipulation that determines the continuous control action under variable contact modes online. We model the physical characteristics of contact-rich manipulation by quasistatic dynamics and complementarity constraints. We then propose a linear complementarity quadratic program (LCQP) to efficiently determine the control action that implicitly includes the decisions on the contact modes under these constraints. In the LCQP, we relax the complementarity constraints to alleviate ill-conditioned problems that are typically caused by measure noises or model miss-matches. We conduct dynamical simulations on a 3D physical simulator and demonstrate that the proposed method can achieve various contact-rich manipulation tasks by determining the control action including the contact modes in real-time.