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A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI

Year: 2026

Type: article

Abstract: 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.

Source: ACM Computing Surveys

DOI: 10.1145/3856802

Authors: Lik Hang Kenny Wong, Xueyang Kang, Kaixin Bai, Jianwei Zhang

Institutions: City University of Hong Kong, Nanyang Technological University, Universität Hamburg

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4415026044

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