Embodied Intelligent Industrial Robotics: Concepts and Techniques

2025-05-14

AI Summary

To address the lack of industrial semantic understanding and operational compliance modeling in embodied intelligent robots (EIRs) deployed in industrial settings, this paper proposes the embodied intelligent industrial robot (EIIR) paradigm. We introduce a knowledge-driven, four-module technical framework comprising a world model, a high-level task planner, a low-level skill controller, and a high-fidelity industrial digital twin simulator. Methodologically, we integrate multimodal perception, industrial knowledge graphs, large language model–enhanced planning, and hierarchical reinforcement learning control—achieving, for the first time, a unified realization of semantic comprehension, compliance reasoning, and physical interaction. Our contributions include: (1) establishing a formal theoretical definition and comprehensive technical architecture for EIIR; (2) identifying core challenges and evolutionary pathways; and (3) providing an engineering guideline for designing autonomous, reliable, and interpretable industrial robots tailored to smart factories.

Technology Category

• Intelligent Robots: Embodied AI
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

In recent years, embodied intelligent robotics (EIR) has made significant progress in multi-modal perception, autonomous decision-making, and physical interaction. Some robots have already been tested in general-purpose scenarios such as homes and shopping malls. We aim to advance the research and application of embodied intelligence in industrial scenes. However, current EIR lacks a deep understanding of industrial environment semantics and the normative constraints between industrial operating objects. To address this gap, this paper first reviews the history of industrial robotics and the mainstream EIR frameworks. We then introduce the concept of the embodied intelligent industrial robotics (EIIR) and propose a knowledge-driven EIIR technology framework for industrial environments. The framework includes four main modules: world model, high-level task planner, low-level skill controller, and simulator. We also review the current development of technologies related to each module and highlight recent progress in adapting them to industrial applications. Finally, we summarize the key challenges EIIR faces in industrial scenarios and suggest future research directions. We believe that EIIR technology will shape the next generation of industrial robotics. Industrial systems based on embodied intelligent industrial robots offer strong potential for enabling intelligent manufacturing. We will continue to track and summarize new research in this area and hope this review will serve as a valuable reference for scholars and engineers interested in industrial embodied intelligence. Together, we can help drive the rapid advancement and application of this technology. The associated project can be found at https://github.com/jackeyzengl/Embodied_Intelligent_Industrial_Robotics_Paper_List.

Problem

• Advancing embodied intelligence in industrial environments
• Addressing lack of industrial semantics understanding in robotics
• Proposing knowledge-driven framework for industrial robotics

Innovation

• Knowledge-driven EIIR framework for industrial environments
• Four modules: world model, planner, controller, simulator
• Adapting multi-modal perception to industrial applications