DrEureka: Language Model Guided Sim-To-Real Transfer
<|reference_start|>DrEureka: Language Model Guided Sim-To-Real Transfer: 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, DrEureka, 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 that 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.<|reference_end|>
@article{ma2024dreureka:,
    title={DrEureka: Language Model Guided Sim-To-Real Transfer},
    author={Yecheng Jason Ma, William Liang, Hung-Ju Wang, Sam Wang, Yuke Zhu,
  Linxi Fan, Osbert Bastani, Dinesh Jayaraman},
    journal={arXiv preprint arXiv:2406.01967},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01967},
    primaryClass={cs.RO cs.AI cs.LG}
}