  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.
