# Whole-Body MPC for a Dynamically Stable Mobile Manipulator

## TL;DR

This paper introduces a whole-body Model Predictive Control framework for mobile manipulators that jointly optimizes manipulation, balance, and interaction tasks, enhancing stability and dexterity in dynamic environments.

## Contribution

The paper presents a novel MPC formulation that transcribes the control problem in end-effector space, integrating contact force planning and stability considerations for unstable robots.

## Key findings

- Effective end-effector pose tracking demonstrated
- Successful door opening in hardware experiments
- Enhanced balance control during manipulation tasks

## Abstract

Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by the manipulator. In this paper, we present a whole-body optimal control framework to jointly solve the problems of manipulation, balancing and interaction as one optimization problem for an inherently unstable robot. The optimization is performed using a Model Predictive Control (MPC) approach; the optimal control problem is transcribed at the end-effector space, treating the position and orientation tasks in the MPC planner, and skillfully planning for end-effector contact forces. The proposed formulation evaluates how the control decisions aimed at end-effector tracking and environment interaction will affect the balance of the system in the future. We showcase the advantages of the proposed MPC approach on the example of a ball-balancing robot with a robotic manipulator and validate our controller in hardware experiments for tasks such as end-effector pose tracking and door opening.