The hybrid nature of multi-contact robotic systems, due to making and breaking contact with the environment, creates significant challenges for high-quality control. Existing model-based methods typically rely on either good prior knowledge of the multi-contact model or require significant offline model tuning effort, thus resulting in low adaptability and robustness. In this paper, we propose a real-time adaptive multi-contact model predictive control framework, which enables online adaption of the hybrid multi-contact model and continuous improvement of the control performance for contact-rich tasks. This framework includes an adaption module, which continuously learns a residual of the hybrid model to minimize the gap between the prior model and reality, and a real-time multi-contact MPC controller. We demonstrated the effectiveness of the framework in synthetic examples, and applied it on hardware to solve contact-rich manipulation tasks, where a robot uses its end-effector to roll different unknown objects on a table to track given paths. The hardware experiments show that with a rough prior model, the multi-contact MPC controller adapts itself on-the-fly with an adaption rate around 20 Hz and successfully manipulates previously unknown objects with non-smooth surface geometries. Accompanying media can be found at: https://sites.google.com/view/adaptive-contact-implicit-mpc/home
PDF@inproceedings{Huang2024,
title = {Adaptive Contact-Implicit Model Predictive Control with Online Residual Learning},
author = {Huang, Wei-Cheng and Aydinoglu, Alp and Jin, Wanxin and Posa, Michael},
year = {2024},
month = may,
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
arxiv = {2310.09893},
url = {https://ieeexplore.ieee.org/document/10610416},
doi = {10.1109/ICRA57147.2024.10610416},
youtube = {kNssB2PIgos},
website = {https://sites.google.com/view/adaptive-contact-implicit-mpc/home}
}