Learning Legged Mobile Manipulation Using Reinforcement Learning.

Many studies on quadrupedal manipulators have been conducted for extending the workspace of the end-effector. Many of these studies, especially the recent ones, use model-based control for the arm and learning-based control for the leg. Some studies solely focused on model-based control for controlling both the base and arm. However, model-based controllers such as MPC can be computationally inefficient when there are many contacts between the end-effector and the object. The dynamics of the interactions between a quadrupedal manipulator and the object in contact are complex and often unpredictable without high-resolution contact sensors on the end-effector. In this study, we investigate the possibility of using a reinforcement learning strategy to control an end-effector of a legged mobile manipulator. The proposed framework is verified for a walking and tracking task of the end-effector in a simulation environment.