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

A Transferable Legged Mobile Manipulation Framework Based on Disturbance Predictive Control