Domain Randomization for Sim2Real Transfer
📰 Lilian Weng's Blog
Domain Randomization (DR) helps close the sim2real gap by randomizing training environment properties
Action Steps
- Train models in a simulator with randomized environment properties
- Apply domain randomization techniques to reduce the sim2real gap
- Test and refine models in real-world scenarios
Who Needs to Know This
Robotics and AI engineers can benefit from DR to improve model transfer to real-world scenarios, making their models more robust and adaptable
Key Insight
💡 Randomizing environment properties in simulators can improve model transfer to real-world scenarios
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💡 Close the sim2real gap with Domain Randomization!
Key Takeaways
Domain Randomization (DR) helps close the sim2real gap by randomizing training environment properties
Full Article
<!-- If a model or policy is mainly trained in a simulator but expected to work on a real robot, it would surely face the sim2real gap. *Domain Randomization* (DR) is a simple but powerful idea of closing this gap by randomizing properties of the training environment. --> <p>In Robotics, one of the hardest problems is how to make your model transfer to the real world. Due to the sample inefficiency of deep RL algorithms and the cost of data collection on real robots, we often need to train model
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