While the exploration for embodied AI has spanned multiple decades, it remains a persistent challenge to endow agents with human-level intelligence, including perception, learning, reasoning, decision-making, control, and generalization capabilities, so that they can perform general-purpose tasks in open, unstructured, and dynamic environments. Recent advances in computer vision, natural language processing, and multi-modality learning have shown that the foundation models have superhuman capabilities for specific tasks. They not only provide a solid cornerstone for integrating basic modules into embodied AI systems but also shed light on how to scale up robot learning from a methodological perspective. This survey aims to provide a comprehensive and up-to-date overview of foundation models in robotics, focusing on autonomous manipulation and encompassing high-level planning and low-level control. Moreover, we showcase their commonly used datasets, simulators, and benchmarks. Importantly, we emphasize the critical challenges intrinsic to this field and delineate potential avenues for future research, contributing to advancing the frontier of academic and industrial discourse.

翻译：尽管对具身智能的探索已跨越数十年，赋予智能体人类水平的智能（包括感知、学习、推理、决策、控制与泛化能力）使其能在开放、非结构化、动态环境中执行通用任务，仍是一个持久挑战。计算机视觉、自然语言处理和多模态学习的最新进展表明，基础模型在特定任务上展现出超人类能力。这类模型不仅为将基础模块整合至具身智能系统提供了坚实基石，更从方法论角度揭示了如何规模化扩展机器人学习的路径。本综述旨在提供机器人领域基础模型的全面且最新的研究概览，聚焦自主操作任务，涵盖高层规划与低层控制。此外，我们展示了该领域常用数据集、仿真平台与基准测试方法。尤为重要的是，我们强调了该领域的核心挑战，并勾勒出未来研究的潜在方向，从而推动学术与产业前沿的发展。