A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI
It offers a comprehensive guide for researchers in Embodied AI to navigate simulation tools, but it is incremental as it builds on existing surveys by focusing on overlooked properties.
This survey analyzes how physics simulators address the sim-to-real gap in robotic navigation and manipulation for Embodied AI, providing a resource with benchmarks and methods to help researchers select tools based on hardware constraints.
Navigation and manipulation are core capabilities in Embodied AI, yet training agents with these capabilities in the real world faces high costs and time complexity. 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 their properties overlooked in previous surveys. We also analyze their features for navigation and manipulation tasks, along with hardware requirements. Additionally, we offer a resource with benchmark datasets, metrics, simulation platforms, and cutting-edge methods-such as world models and geometric equivariance-to help researchers select suitable tools while accounting for hardware constraints.