Contact Optimization for Non-Prehensile Loco-Manipulation via Hierarchical Model Predictive Control

TL;DR

This paper introduces a hierarchical MPC approach for non-prehensile contact optimization in quadruped robot loco-manipulation, enabling precise object control and obstacle avoidance in simulation and real-world tests.

Contribution

The paper proposes a novel hierarchical MPC framework that separates contact force and location optimization from locomotion control for improved manipulation.

Findings

Effective control of object position and orientation with minimal error

Successful obstacle avoidance during loco-manipulation tasks

Validated in both simulation and hardware experiments

Abstract

Recent studies on quadruped robots have focused on either locomotion or mobile manipulation using a robotic arm. Legged robots can manipulate heavier and larger objects using non-prehensile manipulation primitives, such as planar pushing, to drive the object to the desired location. In this paper, we present a novel hierarchical model predictive control (MPC) for contact optimization of the manipulation task. Using two cascading MPCs, we split the loco-manipulation problem into two parts: the first to optimize both contact force and contact location between the robot and the object, and the second to regulate the desired interaction force through the robot locomotion. Our method is successfully validated in both simulation and hardware experiments. While the baseline locomotion MPC fails to follow the desired trajectory of the object, our proposed approach can effectively control both object's position and orientation with minimal tracking error. This capability also allows us to perform obstacle avoidance for both the robot and the object during the loco-manipulation task.