VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control (1802.00265v4)

Abstract: In this paper, we deal 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, we seek 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 simple yet effective shift loss that is agnostic to the downstream task, to constrain the consistency between subsequent frames which is important for consistent policy outputs. We validate the shift loss for artistic style transfer for videos and domain adaptation, and validate our visual control approach in indoor and outdoor robotics experiments.

Real-to-Sim Domain Adaptation for Visual Control in Robotics

The paper "VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control" explores an innovative approach to address the "reality gap" in robotics applications where Deep Reinforcement Learning (DRL) policies trained in simulation are deployed in the real world. Unlike traditional methods that enhance visual fidelity during training, this research proposes adapting real-world sensory inputs to the synthetic domain during deployment, facilitating smoother policy transfer without retraining models across different environments.

The authors introduce a novel solution, termed VR-Goggles, which presents several key advantages:

1. Efficiency in Training: By sidestepping complex preprocessing during DRL training simulations, VR-Goggles significantly reduces computational overhead, ensuring that real-world deployment does not require modifying or retraining the agent when dealing with diverse environments.
2. Flexibility: The decoupling of policy training and domain adaptation allows for parallel operations, improving efficiency. The system requires only minimal data collection from each expected deployment environment to train the VR-Goggles model, enhancing the adaptability of robotic systems across varying real-world scenarios.
3. Shift Loss Implementation: This new concept constrains the consistency between subsequent frames without relying on specific training data sequences. The authors validate shift loss effectiveness in artistic style transfer for videos and domain adaptation scenarios, demonstrating its potential in generating temporally consistent policy outputs for DRL agents.

The paper presents rich quantitative evaluations via the Carla benchmark, whereby policies trained in simulation are transferred to real-world-inspired scenarios without additional training, achieving high success rates with VR-Goggles. Autonomous driving results corroborate this claim, with improved performance metrics compared to standard methods like CycleGAN.

Implications

The implications of this research are significant for robotics, where domain adaptation is pivotal for reliable real-world deployment. The VR-Goggles model reduces the traditional constraints on DRL systems, eliminating the need for environment-specific policy training. This approach can be extrapolated to varied robotic control tasks, enhancing scalability and applicability across sectors from autonomous vehicles to navigation systems in changing environments.

Future Directions

The paper recognizes the potential of expanding this framework beyond navigation to manipulation and more complex robotic tasks, highlighting opportunities for future work. Incorporating VR-Goggles in challenging real-world settings promises to elevate robotics’ ability to dynamically adapt to unforeseen conditions.

In summary, by translating real-world sensing to synthetic environments, this research offers an efficient, repeatable, and robust methodological advancement for deploying AI models trained in simulations to real-life applications, enhancing both theoretical and practical dimensions within the field of robotics.