Virtual Imitation Learning method based on TD3-GAIL for robot manipulator

As robots replace human work, interests in robot teaching are increasing to implement various tasks and to quickly generate task motions. However, there are some difficulties to apply into articulated robots with multiple degrees of freedom. To solve this problem, we developed a virtual environment of robot manipulator for imitation learning and proposed a twin delayed deterministic generative adversarial imitation learning method which provides the imitation of expert's demonstration. The developed virtual environment provides an efficient way to gather expert's demonstration data. The learning performance of the proposed method was verified by comparing it with the conventional one based on the virtual environment for robot manipulators.