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

Fangyi Zhang, Jürgen Leitner, Zongyuan Ge, Michael Milford, Peter Corke

Research output: Contribution to journalArticleResearchpeer-review

25 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1229-1245
Number of pages17
JournalInternational Journal of Robotics Research
Issue number10-11
Publication statusPublished - Sept 2019


  • adversarial transfer learning
  • domain adaptation
  • robotic reaching
  • Sim-to-real transfer
  • visuo-motor policy learning

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