Taskoriented Hierarchical Control for a Quadruped Robot This study aims to build a standard framework that can be applied to accomplish different tasks for a quadruped robot. To achieve this goal, a taskoriented hierarchical control framework is proposed. The framework is composed of four layers, including the task, action, movement, and joint layer, where the action layer plays a crucial role. An action is defined as a group of movements that take the robot from an initial positionorientation to another positionorientation. All actions form the action library, which can be combined to accomplish different tasks. Based on the hierarchical control framework, two tasks including path following and ditch crossing are achieved in this paper. The advantages of the proposed hierarchical control framework are its universality and extendibility since it can be easily applied to other tasks by enriching the action library. Simulation results in VREP are given to validate the effectiveness of the proposed method.

Hierarchical Reinforcement Learning for Quadruped Locomotion Legged locomotion is a challenging task for learning algorithms, especially when the task requires a diverse set of primitive behaviors. To solve these problems, we introduce a hierarchical framework that can automatically learn to decompose complex locomotion tasks. A highlevel policy issues commands in the form of a latent vector and also selects for how long the lowlevel policy will execute the latent command. Concurrently, the lowlevel policy uses the latent command and only the robotxe2x80x99s onboard sensors to control the robotxe2x80x99s actuators. Our approach allows the highlevel policy to run at a lower frequency than the lowlevel one. We test our framework on a pathfollowing task for a dynamic quadruped robot and we show that steering behaviors automatically emerge in the latent command space as lowlevel skills are needed for this task. We then show efficient adaptation of the trained policy to new tasks by transfer of the trained lowlevel policy. Finally, we validate the policies on a real quadruped robot. To the best of our knowledge, this is the first application of endtoend hierarchical learning to a real robotic locomotion task.