Preprints

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 effectiveness of the approach is demonstrated with modular networks in a table-top object reaching task where a 7 DoF arm is controlled in velocity mode to reach a blue cuboid in clutter through visual observations. The adversarial transfer approach reduced the labelled real data requirement by 50%. Policies can be transferred to real environments with only 93 labelled and 186 unlabelled 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.
In IJRR (Under Review), 2018

Selected Publications

. Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies. In IJRR (Under Review), 2018.

Preprint Project Video

. Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies. In ACRA (Best Paper Finalist), 2017.

Preprint PDF Project Video

. Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination. In CVPRW, 2017.

Preprint PDF Project Video

. The ACRV Picking Benchmark: A Robotic Shelf Picking Benchmark to Foster Reproducible Research. In ICRA, 2017.

Preprint PDF Code Dataset Project

. Let the Light Guide Us: VLC-based Localization. In RAM, 2016.

PDF Project

. Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control. In ACRA, 2015.

Preprint PDF Project Video

Academic Service

  • Membership: IEEE Robotics and Automation Society (Student Member)
  • Journal Reviewer:
    • RA-L
    • T-ASE
    • TNNLS
  • Conference Reviewer:
    • ICRA: 2017, 2018
    • IROS: 2017, 2018
    • ICIA: 2016, 2018

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