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World Models

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! Date !! Title !! Authors !! Summary

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| 2017 || [https://arxiv.org/abs/1703.06907 Sim-to-Real Transfer of Robotic Control with Dynamics Randomization] || Josh Tobin et al. || simulated data can be used to train robotic control policies that transfer well to the real world using dynamics randomization, bridging the gap between simulation and real-world data.

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| 2017 || [https://arxiv.org/abs/1612.07828 Learning from Simulated and Unsupervised Images through Adversarial Training] || Ashish Shrivastava et al. || technique that refines simulated images to make them more realistic using adversarial training, enhancing the quality of synthetic data for training robotics models.

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