Multi-contact MPC for Dynamic Loco-manipulation on Humanoid Robots

TL;DR

This paper introduces a multi-contact MPC framework enabling humanoid robots to perform complex loco-manipulation tasks involving multiple contact modes, improving control and task execution efficiency.

Contribution

It presents a simplified multi-contact dynamics model integrated into an MPC framework for dynamic loco-manipulation, capable of handling various contact modes.

Findings

Effective control of humanoid robots in multi-contact loco-manipulation tasks.

The framework requires only contact timings and desired states for operation.

Successful demonstrations include object manipulation during walking and turning.

Abstract

This paper presents a novel method to control humanoid robot dynamic loco-manipulation with multiple contact modes via multi-contact Model Predictive Control (MPC) framework. The proposed framework includes a multi-contact dynamics model capable of capturing various contact modes in loco-manipulation, such as hand-object contact and foot-ground contacts. Our proposed dynamics model represents the object dynamics as an external force acting on the system, which simplifies the model and makes it feasible for solving the MPC problem. In numerical validations, our multi-contact MPC framework only needs contact timings of each task and desired states to give MPC the knowledge of changes in contact modes in the prediction horizons in loco-manipulation. The proposed framework can control the humanoid robot to complete multi-tasks dynamic loco-manipulation applications such as efficiently picking up and dropping off objects while turning and walking.