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README.md

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-[[Website]]( https://eureka-research.github.io/dr-eureka/)
+[[Website]](https://eureka-research.github.io/dr-eureka/)
 [[arXiv (coming soon!)]](https://arxiv.org/abs/2310.12931)
 [[PDF]](https://eureka-research.github.io/dr-eureka/assets/eureka_paper.pdf)
 
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 [![GitHub license](https://img.shields.io/github/license/eureka-research/Eureka)](https://github.com/eureka-research/Eureka/blob/main/LICENSE)
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-<!-- https://github.com/eureka-research/Eureka/assets/21993118/1abb960d-321a-4de9-b311-113b5fc53d4a
 
-![](images/eureka.png) -->
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+https://github.com/eureka-research/DrEureka/assets/21993118/61b25aa1-e39d-4e13-b6c3-2a58e3b23492
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+https://github.com/eureka-research/DrEureka/assets/21993118/47b1b4a2-0ba8-488b-8ae0-2fdf9865bbbe
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 Transferring policies learned in simulation to the real world is a promising strategy for acquiring robot skills at scale. However, sim-to-real approaches typically rely on manual design and tuning of the task reward function as well as the simulation physics parameters, rendering the process slow and human-labor intensive. In this paper, we investigate using Large Language Models (LLMs) to automate and accelerate sim-to-real design. Our LLM-guided sim-to-real approach requires only the physics simulation for the target task and automatically constructs suitable reward functions and domain randomization distributions to support real-world transfer. We first demonstrate our approach can discover sim-to-real configurations that are competitive with existing human-designed ones on quadruped locomotion and dexterous manipulation tasks. Then, we showcase that our approach is capable of solving novel robot tasks, such as quadruped balancing and walking atop a yoga ball, without iterative manual design.
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  author = {Yecheng Jason Ma and William Liang and Hungju Wang and Sam Wang and Yuke Zhu and Linxi Fan and Osbert Bastani and Dinesh Jayaraman}
  year = {2024},
 }
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