Navigation and manipulation are core capabilities in Embodied AI, but training agents to perform them directly in the real world is costly, time-consuming, and unsafe. Therefore, sim-to-real transfer has emerged as a key approach, yet the sim-to-real gap persists. This survey examines how physics simulators address this gap by analyzing properties that have received limited attention in prior surveys. We also analyze their features for navigation and manipulation tasks, as well as their hardware requirements. Additionally, we offer a resource with benchmark datasets, metrics, simulation platforms, and methods to help researchers select suitable tools while accounting for hardware constraints.

翻译：导航与操控是具身智能的核心能力，但直接在真实环境中训练智能体执行这些任务成本高昂、耗时且存在安全隐患。因此，仿真到现实迁移成为关键方法，然而仿真与现实之间的差距依然存在。本综述通过分析以往综述中关注不足的特性，探讨了物理模拟器如何弥合这一差距。同时，我们分析了这些模拟器在导航与操控任务中的特点及其硬件需求。此外，我们还提供了一份包含基准数据集、评估指标、仿真平台及方法的资源，以帮助研究人员在考虑硬件约束的前提下选择合适的工具。