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Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies
[article]
2018
arXiv
pre-print
Various approaches have been proposed to learn visuo-motor policies for real-world robotic applications. One solution is first learning in simulation then transferring to the real world. In the transfer, most existing approaches need real-world images with labels. However, the labelling process is often expensive or even impractical in many robotic applications. In this paper, we propose an adversarial discriminative sim-to-real transfer approach to reduce the cost of labelling real data. The
arXiv:1709.05746v2
fatcat:qaauxfe2ozabzl6uimqxf6mlkm