Contact-Implicit Trajectory Optimization for Dynamic Object Manipulation

TL;DR

This paper introduces a contact-implicit trajectory optimization method for dynamic object manipulation that efficiently computes physically accurate motion plans involving contact sequences for robotic systems.

Contribution

It reformulates contact-implicit optimization with a hard-contact model and complementarity constraints, integrating impact laws and a multiple-shooting scheme for improved computational efficiency.

Findings

Validated on various manipulation tasks with a 6-DOF robot

Achieved physically feasible and accurate trajectories in simulations and hardware

Enhanced computational efficiency using FORCES Pro framework

Abstract

We present a reformulation of a contact-implicit optimization (CIO) approach that computes optimal trajectories for rigid-body systems in contact-rich settings. A hard-contact model is assumed, and the unilateral constraints are imposed in the form of complementarity conditions. Newton's impact law is adopted for enhanced physical correctness. The optimal control problem is formulated as a multi-staged program through a multiple-shooting scheme. This problem structure is exploited within the FORCES Pro framework to retrieve optimal motion plans, contact sequences and control inputs with increased computational efficiency. We investigate our method on a variety of dynamic object manipulation tasks, performed by a six degrees of freedom robot. The dynamic feasibility of the optimal trajectories, as well as the repeatability and accuracy of the task-satisfaction are verified through simulations and real hardware experiments on one of the manipulation problems.