Manipulator Control Method Based on Deep Reinforcement Learning

Abstract

Robotic arm have transformed the manufacturing industry and have been used for scientific exploration in human inaccessible environments. The existing manipulator control methods based on deep reinforcement learning usually discretize the action space or consider the planar manipulator, which results in great limitations of the tasks that the manipulator can accomplish complete. In this paper, we propose a control method based on the Deep Deterministic Policy Gradient (DDPG) algorithm for the 6 degree-of-freedom manipulator that reach the object position in three-dimensional space. This paper designs two types of reward functions, and introduces the manipulability index into the algorithm. The manipulability index evaluates the flexibility of the robotic arm in the work space, which is referenced by the algorithm to optimize the joint pose of the robotic arm to reach the object position. By building a simulation platform to compare the algorithms based on two reward functions, the effectiveness of the DDPG algorithm is verified, and the 6 degree-of-freedom manipulator can reach the object position with more flexible posture based on the DDPG algorithm with manipulability index.