Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies
This reduces labeling costs for robotic applications, though it is incremental as it builds on existing sim-to-real transfer methods.
The paper tackles the problem of transferring visuo-motor policies from simulation to real robots without requiring extensive labeled real-world data, achieving a 50% reduction in labeled data needs and a 97.8% success rate with 1.8 cm accuracy in object reaching tasks.
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.