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

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