The burgeoning fields of robot learning and embodied AI have triggered an
increasing demand for large quantities of data. However, collecting sufficient
unbiased data from the target domain remains a challenge due to costly data
collection processes and stringent safety requirements. Consequently,
researchers often resort to data from easily accessible source domains, such as
simulation and laboratory environments, for cost-effective data acquisition and
rapid model iteration. Nevertheless, the environments and embodiments of these
source domains can be quite different from their target domain counterparts,
underscoring the need for effective cross-domain policy transfer approaches. In
this paper, we conduct a systematic review of existing cross-domain policy
transfer methods. Through a nuanced categorization of domain gaps, we
encapsulate the overarching insights and design considerations of each problem
setting. We also provide a high-level discussion about the key methodologies
used in cross-domain policy transfer problems. Lastly, we summarize the open
challenges that lie beyond the capabilities of current paradigms and discuss
potential future directions in this field.

通过对现有跨域策略转移方法进行系统审查，我们对每个问题设置的总体见解和设计考虑进行了细致的分类，并对跨域策略转移问题中使用的关键方法进行了高层次讨论。最后，我们总结了当前范式能力之外的开放挑战，并讨论了该领域可能的未来发展方向。