提出面向工业场景的具身智能机器人新框架，提升作业效率与安全性。
Embodied intelligent industrial robotics: Framework and techniques
- 基于知识驱动构建五模块架构，融合世界模型与任务规划。
- 在真实装配系统中验证框架可行性，支持复杂工业环境应用。
- 适合智能制造、机器人研发及工业自动化领域研究者参考。
具身智能与机器人的结合具有广阔前景并日益普及。为在工业场景中更高效、准确、可靠且安全地工作，机器人需具备通用知识、环境知识和操作对象知识，这对现有具身智能机器人（EIR）技术构成重大挑战。本文首先简要回顾工业机器人发展历史，分析主流EIR框架的局限性；随后提出一种新的知识驱动型具身智能工业机器人（EIIR）技术框架，适用于多种工业环境。该框架包含五个模块：世界模型、高层任务规划器、低层技能控制器、仿真器和物理系统。文中详细回顾了各模块相关技术的发展，并讨论其向工业应用适配的最新进展。通过一个真实装配系统的案例研究，验证了所提EIIR框架的适用性与潜力。最后总结了EIIR在工业场景中面临的关键挑战，并提出了未来研究方向。作者认为，EIIR技术正塑造下一代工业机器人，基于EIIR的工业系统为智能制造提供了全新技术范式。本综述可为关注工业具身智能的研究者与工程师提供重要参考，助力快速推进与应用。作者将持续跟踪并贡献该项目进展，详见项目页：https://github.com/jackyzengl/EIIR。
原文摘要 · Abstract (English)
The combination of embodied intelligence and robots has great prospects and is becoming increasingly common. In order to work more efficiently, accurately, reliably, and safely in industrial scenarios, robots should have at least general knowledge, working-environment knowledge, and operating-object knowledge. These pose significant challenges to existing embodied intelligent robotics (EIR) techniques. Thus, this paper first briefly reviews the history of industrial robotics and analyzes the limitations of mainstream EIR frameworks. Then, a new knowledge-driven technical framework of embodied intelligent industrial robotics (EIIR) is proposed for various industrial environments. It has five modules: a world model, a high-level task planner, a low-level skill controller, a simulator, and a physical system. The development of techniques related to each module are also thoroughly reviewed, and recent progress regarding their adaption to industrial applications are discussed. A case study of real-world assembly system is given to demonstrate the newly proposed EIIR framework's applicability and potentiality. Finally, the key challenges that EIIR encounters in industrial scenarios are summarized and future research directions are suggested. The authors believe that EIIR technology is shaping the next generation of industrial robotics and EIIR-based industrial systems supply a new technological paradigm for intelligent manufacturing. It is expected that this review could serve as a valuable reference for scholars and engineers that are interested in industrial embodied intelligence. Together, scholars can use this research to drive their rapid advancement and application of EIIR techniques. The authors would continue to track and contribute new studies in the project page https://github.com/jackyzengl/EIIR
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