Learning-Based Joint Control with Hierarchical Reinforcement Learning and On-Device Execution

In typical robot learning, deep reinforcement learning policies are employed in the upper control layer to generate target joint angles for robot motion, while conventional controllers are used in the fast lower control layer to control each joint motor. This paper presents a fully neural network-based hierarchical reinforcement learning approach for real-time robot joint control. The proposed method divides joint control into two layers: a high-frequency current control policy and a low-frequency position control policy. The current control policy drives the motor to follow the target current while learning the dynamic characteristics of the joint. The position control policy generates the target current to achieve a desired joint angle, allowing learning and inference at a slower frequency. By decoupling motor dynamics from position control, our method improves learning performance and enables policy generalization across joints. Experimental results on a three-joint robotic arm demonstrate the effectiveness of the proposed approach, including posture control using a shared position control policy across joints.