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arXiv · 2406.01967

DrEureka: Language Model Guided Sim-To-Real Transfer

Yecheng Jason Ma·William Liang·Hung-Ju Wang·Sam Wang·Yuke Zhu·Linxi Fan·Osbert Bastani·Dinesh Jayaraman
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Abstract

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.

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Yecheng Jason Ma, William Liang, Hung-Ju Wang, Sam Wang, Yuke Zhu, Linxi Fan, Osbert Bastani, Dinesh Jayaraman. 2024-06-04. DrEureka: Language Model Guided Sim-To-Real Transfer. https://arxiv.org/abs/2406.01967

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