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.

翻译：机器人学习与具身人工智能的蓬勃发展导致对大量数据的需求日益增长。然而，由于数据收集成本高昂且安全要求严苛，从目标领域收集充足的无偏数据仍具挑战。因此，研究者常借助模拟环境、实验室等易获取源领域的数据，以实现低成本数据采集和快速模型迭代。但此类源领域的环境与具身体与目标领域存在显著差异，凸显了跨域策略迁移方法的必要性。本文系统梳理了现有跨域策略迁移方法，通过细粒度分类域间隙，归纳各问题设定的总体思路与设计考量，并对跨域策略迁移问题的核心方法论进行高阶探讨。最后，本文总结了当前范式难以克服的开放挑战，并讨论了该领域的潜在未来方向。