  Due to their ability to adapt to different terrains, quadruped robots have
drawn much attention in the research field of robot learning. Legged mobile
manipulation, where a quadruped robot is equipped with a robotic arm, can
greatly enhance the performance of the robot in diverse manipulation tasks.
Several prior works have investigated legged mobile manipulation from the
viewpoint of control theory. However, modeling a unified structure for various
robotic arms and quadruped robots is a challenging task. In this paper, we
propose a unified framework disturbance predictive control where a
reinforcement learning scheme with a latent dynamic adapter is embedded into
our proposed low-level controller. Our method can adapt well to various types
of robotic arms with a few random motion samples and the experimental results
demonstrate the effectiveness of our method.
