The realization of universal robots is an ultimate goal of researchers. However, a key hurdle in achieving this goal lies in the robots' ability to manipulate objects in their unstructured surrounding environments according to different tasks. The learning-based approach is considered an effective way to address generalization. The impressive performance of foundation models in the fields of computer vision and natural language suggests the potential of embedding foundation models into manipulation tasks as a viable path toward achieving general manipulation capability. However, we believe achieving general manipulation capability requires an overarching framework akin to auto driving. This framework should encompass multiple functional modules, with different foundation models assuming distinct roles in facilitating general manipulation capability. This survey focuses on the contributions of foundation models to robot learning for manipulation. We propose a comprehensive framework and detail how foundation models can address challenges in each module of the framework. What's more, we examine current approaches, outline challenges, suggest future research directions, and identify potential risks associated with integrating foundation models into this domain.

翻译：实现通用机器人是研究人员的终极目标。然而，实现这一目标的关键障碍在于机器人能否根据不同的任务，在其非结构化的周围环境中操作物体。基于学习的方法被认为是解决泛化问题的有效途径。基础模型在计算机视觉和自然语言领域的卓越表现表明，将基础模型嵌入操作任务中，是实现通用操作能力的可行路径。然而，我们认为，实现通用操作能力需要一个类似于自动驾驶的总体框架。该框架应包含多个功能模块，不同的基础模型在促进通用操作能力方面扮演不同角色。本综述聚焦于基础模型对机器人操作学习的贡献。我们提出了一个综合框架，并详细阐述了基础模型如何应对该框架中每个模块的挑战。此外，我们审视了当前方法，概述了挑战，提出了未来研究方向，并指出了将基础模型集成到该领域可能涉及的潜在风险。