MOMA-Force: Visual-Force Imitation for Real-World Mobile Manipulation

TL;DR

MOMA-Force introduces a visual-force imitation approach that combines perception, imitation learning, and admittance control to enable mobile manipulators to perform contact-rich tasks with high success and low force impact in real-world settings.

Contribution

The paper presents MOMA-Force, a novel method integrating perception, imitation learning, and control for robust, contact-rich mobile manipulation, outperforming baseline methods.

Findings

Higher task success rates in household settings.

Smaller contact forces and force variances achieved.

Effective combination of learning and classical control.

Abstract

In this paper, we present a novel method for mobile manipulators to perform multiple contact-rich manipulation tasks. While learning-based methods have the potential to generate actions in an end-to-end manner, they often suffer from insufficient action accuracy and robustness against noise. On the other hand, classical control-based methods can enhance system robustness, but at the cost of extensive parameter tuning. To address these challenges, we present MOMA-Force, a visual-force imitation method that seamlessly combines representation learning for perception, imitation learning for complex motion generation, and admittance whole-body control for system robustness and controllability. MOMA-Force enables a mobile manipulator to learn multiple complex contact-rich tasks with high success rates and small contact forces. In a real household setting, our method outperforms baseline methods in terms of task success rates. Moreover, our method achieves smaller contact forces and smaller force variances compared to baseline methods without force imitation. Overall, we offer a promising approach for efficient and robust mobile manipulation in the real world. Videos and more details can be found on \url{https://visual-force-imitation.github.io}