MOMA-Force: Visual-Force Imitation for Real-World Mobile Manipulation
Taozheng Yang Ya Jing Hongtao Wu Jiafeng Xu Kuankuan Sima Guangzeng Chen Qie Sima Tao Kong
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}