Adversarial discriminative sim-to-real transfer of visuo-motor policies

• Published in: The International journal of robotics research 2019-09, Vol.38 (10-11), p.1229-1245
• Main Authors: Zhang, Fangyi, Leitner, Jürgen, Ge, Zongyuan, Milford, Michael, Corke, Peter
• Format: Article
• Language: English
• Subjects: Clutter; Labels; Policies; Robotics; Visual observation
• ISSN: 0278-3649; 1741-3176
• DOI: 10.1177/0278364919870227

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 labeling process is often expensive or even impractical in many robotic applications. In this article, we introduce an adversarial discriminative sim-to-real transfer approach to reduce the amount of labeled real data required. The effectiveness of the approach is demonstrated with modular networks in a table-top object-reaching task where a seven-degree-of-freedom arm is controlled in velocity mode to reach a blue cuboid in clutter through visual observations from a monocular RGB camera. The adversarial transfer approach reduced the labeled real data requirement by 50%. Policies can be transferred to real environments with only 93 labeled and 186 unlabeled real images. The transferred visuo-motor policies are robust to novel (not seen in training) objects in clutter and even a moving target, achieving a 97.8% success rate and 1.8 cm control accuracy. Datasets and code are openly available.