GadgetArm—Automatic Grasp Generation and Manipulation of 4-DOF Robot Arm for Arbitrary Objects Through Reinforcement Learning

Abstract

Automatic robot gripper system which involves the automated object recognition of work-in-process in production line is the key technology of the upcoming manufacturing facility achieving Industry 4.0. Automatic robot gripper enables the manufacturing system to be autonomous, self-recognized, and adaptable by using artificial intelligence of robot programming dealing with arbitrary shapes of work-in-processes. This paper specifically explores the chain of key technologies, such as 3D object recognition with CAD and point cloud data, reinforcement learning of robot arm, and customized 3D printed gripper, in order to enhance the intelligence of the robot controller system. And it also proposes the integration with 3D point cloud based object recognition and game-engine based reinforcement learning. The result of the prototype of the intelligent robot gripping system developed by the proposed method with a 4 degree-of-freedom robot arm is explained in this paper.

Constant Force-Tracking Control Based on Deep Reinforcement Learning in Dynamic Auscultation Environment

“…In recent years, the application of deep reinforcement learning [ 3 , 4 , 5 , 6 , 7 , 8 ] in the robot field [ 9 , 10 ] has deepened and has been widely used in grasping [ 11 , 12 ], assembly [ 13 ], path planning [ 14 , 15 ], and other fields [ 16 , 17 ]. A few scholars have used deep reinforcement learning to study the constant force-tracking process, showing the great potential for applying deep reinforcement learning to solving the issue of constant force-tracking.…”