Cyber–physical systems (CPSs) used in safety-critical applications must safely operate even in unpredictable situations. The functional safety of their physical components, which have relatively low complexity, has been significantly assured through the safety standards. However, it is still difficult to ensure the intended functional safety (IFS) of autonomous control components driven by artificial intelligence models, as only a limited number of scenarios can be realized by engineers. This issue may cause the fatal injuries and the material damages under the unconsidered scenarios. To address this issue, we propose a generative digital twin (gDT) that continually enhances the IFS of the CPSs using a deep reinforcement learning algorithm and a retraining scheme. The workflow of the gDT involves 1) generating unknown hazardous scenarios that engineers cannot consider during the development phase and 2) feeding the generated scenarios back to the autonomous control components. Experimental results show that the proposed digital twin can discover the unknown hazardous scenarios against prototypes of the CPSs and can enhance the infraction avoiding rate to 90% against the discovered ones while maintaining the ability to operate in the existing known safe ones.

High-Mix Low-Volume (HMLV) manufacturing increasingly relies on heterogeneous robot fleets, but automatic generation of vendor-specific robot control code remains difficult due to platform fragmentation and safety-critical feasibility constraints. Although recent Large Language Model (LLM)-based approaches have shown promise for translating natural language into robot programs, they remain largely limited to single-platform or simulation-oriented settings and are vulnerable to physical hallucination, including spatially inconsistent commands and dynamically infeasible motions. This paper proposes a Digital Twin-integrated verification framework for adaptive control code generation in heterogeneous robot systems. The framework uses a structured intermediate task representation to support runtime spatial grounding, robot selection, pre-execution dynamics validation, and adaptive motion scaling before vendor-specific code generation and execution. Evaluation on 170 task-description scenarios and eight robot selection tasks showed improved ranking discriminability in lightweight stress cases where conventional baselines exhibited limited separation. In addition, adaptive dynamics scaling enabled safe execution in all analytically verified test cases, compared with 50% without scaling. These results suggest that Digital Twin-grounded verification and adaptive feasibility control can improve the reliability of LLM-based multi-vendor robot programming and help mitigate physical hallucination in heterogeneous robot systems.

As network complexity increases because of the diversification of applications and services based on internet of autonomous things, it is difficult for humans to design the optimal control rule for software-defined networking controllers. Intelligence-defined networking, called IDN, is proposed to overcome this limitation through machine learning algorithms. Since the existing IDN approaches are mostly designed to optimize only the network quality of services, including throughput, jitter, and latency, the controllers don’t consider the importance of data in the applications and the services. This causes the controller to allocate insufficient resources to the crucial data flow, which leads critical problems, such as self-driving car accidents. To prevent this problem, we propose an importance-based IDN architecture that enables network controllers to manage network traffic with importance levels of data flows. Firstly, we devise an importance estimation scheme to set the importance level for the flows. Secondly, a dynamic resource allocation model of the controllers is developed by means of deep learning algorithms in order to make the optimal network resource. Additionally, an online learning mechanism based on weighted auto-labeling is adopted to continue enhancing the adaptability of the resource allocation model on runtime as the network conditions change. The evaluation results of the proposed architecture under various autonomous things scenarios show that the loss rate for data flows of higher importance is reduced by one-quarter compared to the case of a network controller without importance level and that the bandwidth waste ratio is reduced by ten percent compared to the rule-based model.

Robotic manipulators were initially introduced to replace repetitive human labor and have since evolved to perform complex tasks in dynamic environments. In such systems, imitation learning and reinforcement learning models capable of real-time trajectory generation are widely applied. Among these approaches, imitation learning enables rapid training when high-quality datasets are available. However, it suffers from high costs associated with collecting expert demonstration data and significant performance variability depending on data quality. Recently, learning approaches utilizing large-scale datasets have been explored, but they often struggle to guarantee reliable performance in tasks requiring precise control and incur substantial computational costs for model construction, limiting their applicability as a general-purpose learning strategy. To address these limitations, this paper proposes an imitation learning framework that integrates sampling-based motion planning with a hybrid data curation strategy. The proposed method employs a sampling-based planner (e.g., RRT*) to generate diverse physically feasible trajectories, thereby reducing the cost of acquiring expert demonstration data. The generated trajectories are then curated through clustering-based grouping and rule-based filtering to select high-quality training samples from large-scale datasets. The proposed framework automatically generates physically feasible trajectories while selecting high-quality data from large trajectory pools, thereby improving training stability and reducing data-related costs. Experimental results demonstrate that the proposed method achieves an average success rate of 79.1% (95% CI: 74.3–83.2%) and produces trajectories with shorter trajectories, lower final distances, and reduced joint movements compared to conventional filtering methods.