Adaptive Contact-Implicit Model Predictive Control with Online Residual Learning

• Published in: 2024 IEEE International Conference on Robotics and Automation (ICRA) pp. 5822 - 5828
• Main Authors: Huang, Wei-Cheng, Aydinoglu, Alp, Jin, Wanxin, Posa, Michael
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.05.2024
• Subjects: Adaptation models; Adaptive systems; Geometry; Hardware; Real-time systems; Task analysis; Uncertainty
• DOI: 10.1109/ICRA57147.2024.10610416

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