With the rapid advancement of generative artificial intelligence, embodied intelligence is increasingly being integrated into robotic tasks within smart manufacturing. Industrial operations typically feature long-horizon sequences and demand high precision. However, although embodied models, particularly Vision-Language-Action (VLA) models, have shown significant promise in autonomous control with superior generalization capabilities, their inherent black-box nature lacks the transparency and trustworthiness essential for manufacturing tasks. To address this challenge, we propose the Industrial Embodied Intelligence Trustworthy Interpretable Agent (IEI-TIA), a high-level trustworthy supervisory agent designed for industrial sorting and palletizing tasks. Rather than directly generating low-level control commands, IEI-TIA monitors robotic execution, diagnoses possible failure types at key operational stages, and provides corrective next-step instruction via invoked skills, thereby improving the reliability and interpretability of long-horizon and repetitive robotic tasks. An alignment-enhanced parameter-efficient finetuning method is proposed utilizing paired vision-language data to enable robust trustworthiness identification across various possible robotic failure types in different subtasks in sorting and palletizing. Additionally, we construct a specialized dataset and establish benchmarks for industrial embodied intelligence behavior diagnosis and instructions. Experimental results demonstrate that the fine-tuned trustworthy agent achieves 93.7% diagnosis accuracy for various task failures, representing a substantial improvement of approximately 38% over baseline general-purpose large models. By integrating the agent’s step-by-step operational instructions, the success rate of robotic long-horizon and repetitive tasks is improved by 14.0%.

To meet higher requirements of flexible manufacturing, smart manufacturing is developing to intelligently deal with changing demands in product customization with better generalization and adaptation. For instance, robotic systems are anticipated to realize embodied and spatial intelligence in manufacturing, to intelligently generalize in handling diverse objects in changing environments. However, the insufficiency of 3D scene data significantly hinders embodied and spatial intelligence learning. Therefore, based on digital twin, the digital genealogy is proposed to generate more diverse synthetic data, rather than synchronizing the same scene with physical world by digital twin. Also, the digital genealogy focuses on the whole evolution process from industrial parts to products in manufacturing, rather than narrowly focusing on current state in digital twin. In digital genealogy, DG-DNA for various industrial parts, similar with biology, is proposed to constrain reliable generation results of parts. To generate digital genealogy scenes with diverse industrial parts, parts matching and generation methods are both adopted with constraints of DG-DNA. Specifically, an artificial intelligence generative algorithm, named DGIP-Gen, is proposed to generate target industrial part given specific DG-DNA. The experimental results have demonstrated the generated parts are diverse and meet the specific constraints of different DG-DNA requirements, to support embodied and spatial intelligence learning.

With the continuous expansion of industrial systems, multisource and heterogeneous industrial data have increased rapidly, making the construction of a structured industrial knowledge system a core requirement in the industrial domain. Industrial knowledge graph (IKG) serves as a key approach for knowledge structuring and relation modeling and has become an indispensable foundation for industrial tasks. However, existing IKG construction methods still face core challenges such as data heterogeneity, complex semantic understanding, frequent knowledge changes, and limited automation. Inspired by the construction of IKG by industry experts, we propose CoMA-IKG, an large language model (LLM)-driven collaborative multiagent framework for automated construction of IKG. In the industrial data processing stage, an LLM-driven adaptive chunking agent is developed to achieve semantically complete and self-adjusting segmentation. In the triple extraction stage, a cluster of LLM-driven agents for progressive triple reasoning extraction and mechanism-aware logical discrimination is constructed to enable accurate industrial triple extraction under stepwise reasoning and industrial mechanism constraints. In the IKG evolution stage, an LLM-driven co-evolution agent is developed to generate evolution commands automatically based on the structural state of the IKG and real-time industrial data changes, enabling autonomous updating and continuous evolution of the IKG. Experimental results show that CoMA-IKG significantly outperforms existing automated knowledge graph construction methods in terms of relation mining, logical reasoning, and dynamic evolution of the IKG.

Grasping tasks are crucial in industrial manufacturing, where precise and efficient object grasping ensures smooth assembly processes and stable operations. Robotic arms, deployed in industrial environments, require timely and accurate computations to perform these tasks. This paper introduces BGRN, an RGB-D fusion-based binary grasp prediction network designed for lightweight grasp pose prediction in such settings. We propose a binary grasp pose prediction framework that significantly reduces memory usage by quantizing both weights and activations to 1 bit. Additionally, an interaction fusion module improves the integration of RGB and depth images, while an information recovery connection helps mitigate feature loss caused by binarization. Experimental results show that BGRN achieves competitive accuracy and notable reductions in memory usage and computational load compared to full-precision models.

Recently, foundation models (such as ChatGPT) have emerged with powerful learning, understanding, and generalization abilities, showcasing tremendous potential to revolutionarily promote modern industry. Despite significant advancements in various fields, existing general foundation models face challenges in industry when dealing with the data of specialized modalities, the tasks of varying-scenario with multiple processes, and the requirements of trustworthy output, which makes industrial foundation model (IFM) a necessity. This article proposes a system architecture of termed IFMsys, including model training, model adaptation, and model application. Specifically, in model training, a base model is constructed by pretraining on multimodal industrial data and fine-tuning with fundamental industrial mechanisms. In model adaptation, the base model is developed into a series of task-oriented and domain-specific IFMs through fine-tuning with representative tasks and domain knowledge. In model application, an industrial agent-centric collaboration system and a comprehensive application framework of IFM are proposed to enhance the industrial product lifecycle applications. In addition, a prototype system of the IFM, namely, MetaIndux, is delivered, with application examples presented in typical industrial tasks. Finally, future research directions and open issues of IFM are prospected. We hope this article will inspire the advancements in the theories, technologies, and applications in this emerging research field of IFM.

Digital twin has made significant achievements in applications across various fields by bridging the gap between virtual and physical world. However, the modeling of digital twin faces challenges of labor-intensive and time-consuming manual modeling or coding. With the emergence of artificial intelligence foundation models, the integration of large language models (LLMs) and digital twin may contribute to enhancing modeling efficiency significantly. Therefore, in this paper, a framework of digital twin modeling code generation is proposed based on LLMs. Given instructions in natural language prompts, modeling code in digital twin software will be generated from LLMs. Firstly, a tree diagram-based code generation method is proposed to generate main section of modeling code. Additionally, a randomized code completion method is proposed to complete missing parts which are unspecified or unclear in prompts. Finally, a case study is given in NVIDIA Omniverse for a digital twin construction based on framework proposed in this paper.

Investigating the methods of handling real-world objects with robotic arms is a crucial aspect of artificial intelligence research. By inputting images from cameras or other devices, neural networks extract suitable end-effector poses to achieve precise object manipulation. Current methods face challenges of inadequate regression precision and excessive classification, which negatively impact network performance. Thus, we propose a new method based on the local grasp unit. This approach transforms grasp prediction into evaluating whether the features of local grasp units are suitable for grasping. Stable local grasp units are selected by introducing a binary grasp pose discriminator with local point clouds as inputs. In addition, to tackle the issue of distant regions in point clouds of grasp pose discriminator, we propose the innovative Attention-Bridging Layer. The Attention-Bridging Layer selects representative points from different regions and enables information exchange between them, facilitating information flow between distant areas and enhancing the network’s performance.

Smart manufacturing has been transforming toward industrial digitalization integrated with various advanced technologies. Metaverse has been evolving as a next-generation paradigm of a digital space extended and augmented by reality. In the metaverse, users are interconnected for various virtual activities. In consideration of advanced possibilities that may be brought by the metaverse, it is envisioned that industrial metaverse should be integrated into smart manufacturing to upgrade industry for more visible, intelligent and efficient production in the future. Therefore, a conceptual model, named IMverse Model, and novel characteristics of the industrial metaverse for smart manufacturing are proposed in this article. Besides, an industrial metaverse architecture, named IMverse Architecture, is proposed involving several key enabling technologies. Typical innovative applications of the industrial metaverse throughout the whole product life cycle for smart manufacturing are presented with insights. Nonetheless, in prospect of future, the industrial metaverse still faces limitations and is far from implementation. Thus, challenges and open issues of the industrial metaverse for smart manufacturing are discussed, then outlook is provided for further research and application.

Industrial Internet of Things data incorporate the fundamental elements of industrial processes, providing novel paradigms of predictive maintenance for complex industrial equipment. Remaining useful life prediction is critical in the predictive maintenance task of product lifecycle management, which has attracted increasing research attention. However, most existing prediction methods cannot effectively extract complex multiscale temporal patterns and cannot meet the real-time requirements of industrial sites. To address these issues, we propose a$T^{2}$-Tensor-aided multiscale transformer for accurate and effective prediction in this article. We defined the$T^{2}$-tensor to represent the multiscale temporal pattern by reconstructing the time series. Besides, a high-order transformer for multiscale feature extraction is proposed. Particularly, the multiscale characteristics can be captured through intertoken and intratoken. In addition, a transformer parameter lightweighting method with tensor ring decomposition is developed. Experiments demonstrate the accuracy and efficiency of the proposed method.

Integration of each aspect of the manufacturing process with the new generation of information technology such as the Internet of Things, big data, and cloud computing makes industrial manufacturing systems more flexible and intelligent. Industrial big data, recording all aspects of the industrial production process, contain the key value for industrial intelligence. For industrial manufacturing, an essential and widely used electronic device is the lithium-ion battery (LIB). However, accurately predicting the remaining useful life (RUL) of LIB is urgently needed to reduce unexpected maintenance and avoid accidents. Due to insufficient amount of degradation data, the prediction accuracy of data-driven methods is greatly limited. Besides, mathematical models established by model-driven methods to represent degradation process are unstable because of external factors like temperature. To solve this problem, a new LIB RUL prediction method based on improved convolution neural network (CNN) and long short-term memory (LSTM), namely Auto-CNN-LSTM, is proposed in this article. This method is developed based on deep CNN and LSTM to mine deeper information in finite data. In this method, an autoencoder is utilized to augment the dimensions of data for more effective training of CNN and LSTM. In order to obtain continuous and stable output, a filter to smooth the predicted value is used. Comparing with other commonly used methods, experiments on a real-world dataset demonstrate the effectiveness of the proposed method.