There has been a resurgence of interest on generative adversarial networks (GANs) in recent years. The overall performance of the generator depends on how well the discriminator is trained. In this study, we use two discriminators and one generator in the adversarial architecture. Each discriminator has a different perspective of how to evaluate the generated data, which makes the dual discriminators compete with each other to improve the performance of the generator output. The competition of the discriminators is evaluated using a zero-sum game in order to make the optimizer converge into the Nash-equilibrium. Experimental results show that the proposed approach has better visual performance and the EM-distance metric over some well-known GAN models, including DCGAN, BEGAN, WGAN, and WGAN-GP.
In order to develop a real-time teleoperated robotic system it is necessary to cope with the latency that is contributed by all the components of the system. Usually, the transmission elapsed time is one of the most serious issues in the latency problem. In the proposed teleoperated robotic system, there are a motion-capture system to acquire human motion data, a paddle-juggling robot to bounce a ping-pong ball up and down, and a proposed latency-removal algorithm to remove the motion latency between the human and the robot. It is assumed that the delay induced by the latency-removal algorithm is negligible. To reduce the lump-sum latency effect of the teleoperated system, the proposed latency-removal algorithm is based on a emulator/controller neural-network architecture to predict the robot motion command in advance. In the task of bouncing a ping-pong ball up and down, the movement is near cyclic. The simulation result shows that the performance by the proposed method is even better for a near cyclic movement.
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