Abstract
The rapid emergence of foundation models, particularly Large Language Models (LLMs) and Vision-Language Models (VLMs), has introduced a transformative paradigm in robotics. These models offer powerful capabilities in semantic understanding, high-level reasoning, and cross-modal generalization, enabling significant advances in perception, planning, control, and human-robot interaction. This critical review provides a structured synthesis of recent developments, categorizing applications across simulation-driven design, open-world execution, sim-to-real transfer, and adaptable robotics. Unlike existing surveys that emphasize isolated capabilities, this work highlights integrated, system-level strategies and evaluates their practical feasibility in real-world environments. Key enabling trends such as procedural scene generation, policy generalization, and multimodal reasoning are discussed alongside core bottlenecks, including limited embodiment, lack of multimodal data, safety risks, and computational constraints. Through this lens, this paper identifies both the architectural strengths and critical limitations of foundation model-based robotics, highlighting open challenges in real-time operation, grounding, resilience, and trust. The review concludes with a roadmap for future research aimed at bridging semantic reasoning and physical intelligence through more robust, interpretable, and embodied models.
Abstract (translated)
基础模型（尤其是大型语言模型和视觉-语言模型）的迅速发展为机器人技术带来了变革性的范式。这些模型提供了强大的语义理解、高级推理和跨模态泛化能力，从而在感知、规划、控制以及人机交互方面取得了重大进展。这篇批判性综述系统地总结了最近的发展，并根据模拟驱动设计、开放环境执行、从模拟到现实的迁移以及适应性机器人等应用对这些发展进行了分类。与强调孤立功能的现有调查不同，这项工作突出了集成的、系统级策略，并评估了它们在实际环境中的可行性。 文中还讨论了一些关键的技术趋势，如程序化场景生成、政策泛化和多模态推理，同时指出了核心瓶颈问题，包括有限的身体化能力、缺乏多模态数据、安全风险以及计算限制。通过这一视角，本文不仅识别出基于基础模型的机器人架构的优势和局限性，还强调了实时操作、实体化、弹性和信任方面的开放挑战。 综述最后提出了一条未来研究路线图，旨在通过更强大、可解释且具身体化的模型来弥合语义推理与物理智能之间的差距。
URL
https://arxiv.org/abs/2507.10087
