Title: Virtual-to-Real: Learning to Control in Visual Semantic Segmentation,
Abstract: Collecting training data from the physical world is usually time-consuming
and even dangerous for fragile robots, and thus, recent advances in robot
learning advocate the use of simulators as the training platform.
Unfortunately, the reality gap between synthetic and real visual data prohibits
direct migration of the models trained in virtual worlds to the real world.
This paper proposes a modular architecture for tackling the virtual-to-real
problem. The proposed architecture separates the learning model into a
perception module and a control policy module, and uses semantic image
segmentation as the meta representation for relating these two modules. The
perception module translates the perceived RGB image to semantic image
segmentation. The control policy module is implemented as a deep reinforcement
learning agent, which performs actions based on the translated image
segmentation. Our architecture is evaluated in an obstacle avoidance task and a
target following task. Experimental results show that our architecture
significantly outperforms all of the baseline methods in both virtual and real
environments, and demonstrates a faster learning curve than them. We also
present a detailed analysis for a variety of variant configurations, and
validate the transferability of our modular architecture.