Model Predictive Robot-Environment Interaction Control for Mobile Manipulation Tasks

TL;DR

This paper introduces an adaptive MPC-based control framework for mobile manipulators that enables effective interaction with unknown environments without re-tuning or pre-modeling, demonstrated through various manipulation tasks.

Contribution

It combines adaptive schemes with MPC to improve robot-environment interaction handling without prior environment modeling or parameter tuning.

Findings

Successful interaction with unknown environments demonstrated

Enhanced robustness and flexibility in manipulation tasks

Validated on a ball-balancing manipulator for door opening and lifting

Abstract

Modern, torque-controlled service robots can regulate contact forces when interacting with their environment. Model Predictive Control (MPC) is a powerful method to solve the underlying control problem, allowing to plan for whole-body motions while including different constraints imposed by the robot dynamics or its environment. However, an accurate model of the robot-environment is needed to achieve a satisfying closed-loop performance. Currently, this necessity undermines the performance and generality of MPC in manipulation tasks. In this work, we combine an MPC-based whole-body controller with two adaptive schemes, derived from online system identification and adaptive control. As a result, we enable a general mobile manipulator to interact with unknown environments, without any need for re-tuning parameters or pre-modeling the interacting objects. In combination with the MPC controller, the two adaptive approaches are validated and benchmarked with a ball-balancing manipulator in door opening and object lifting tasks.