  The potential diagnostic applications of magnet-actuated capsules have been
greatly increased in recent years. For most of these potential applications,
accurate position control of the capsule have been highly demanding. However,
the friction between the robot and the environment as well as the drag force
from the tether play a significant role during the motion control of the
capsule. Moreover, these forces especially the friction force are typically
hard to model beforehand. In this paper, we first designed a magnet-actuated
tethered capsule robot, where the driving magnet is mounted on the end of a
robotic arm. Then, we proposed a learning-based approach to model the friction
force between the capsule and the environment, with the goal of increasing the
control accuracy of the whole system. Finally, several real robot experiments
are demonstrated to showcase the effectiveness of our proposed approach.
