  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.
