Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight

@article{Kang2019GeneralizationTS,
  title={Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight},
  author={Katie Kang and Suneel Belkhale and Gregory Kahn and P. Abbeel and Sergey Levine},
  journal={2019 International Conference on Robotics and Automation (ICRA)},
  year={2019},
  pages={6008-6014}
}
Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quantity and variety of data available for training. This data can be difficult to obtain for some types of robotic systems, such as fragile, small-scale quadrotors. Simulated rendering and physics can provide for much larger datasets, but such data is inherently of lower quality: many of the phenomena that make the real… 

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