VR Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control

This paper deals with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks. Instead of adopting the common solutions to the problem by increasing the visual fidelity of synthetic images output from simulators during the training phase, this paper seeks to tackle the problem by translating the real-world image streams back to the synthetic domain during the deployment phase, to make the robot feel at home. We propose this as a lightweight, flexible, and efficient solution for visual control, as 1) no extra transfer steps are required during the expensive training of DRL agents in simulation; 2) the trained DRL agents will not be constrained to being deployable in only one specific real-world environment; 3) the policy training and the transfer operations are decoupled, and can be conducted in parallel. Besides this, we propose a conceptually simple yet very effective shift loss to constrain the consistency between subsequent frames, eliminating the need for optical flow. We validate the shift loss for artistic style transfer for videos and domain adaptation, and validate our visual control approach in realworld robot experiments. A video of our results is available at: https://goo.gl/b1xz1s.