Cover Caption: The cover image is based on the Research Article A deep transfer-learning-based dynamic reinforcement learning for intelligent tightening system by Wentao Luo et al., https://doi.org/10.1002/int.22345.

Reinforcement learning (RL) has been widely applied in the static environment with standard reward functions. For intelligent tightening tasks, it is a challenge to transform expert knowledge into a recognizable mathematical expression for RL agents. Changing assembly standards make the model repeat learning updated knowledge with a high time-cost. In addition, as the difficulty and low accuracy of designing reward functions, the RL model itself also limits its application in the complex and dynamic engineering environment. To solve the above problems, a deep transfer-learning-based dynamic reinforcement learning (DRL-DTL) is presented and applied in the intelligent tightening system. Specifically, a deep convolution transfer-learning model (DCTL) is presented to build a mathematical mapping between agents of the model and subjective knowledge, which endows agents to learn from human knowledge efficiently. Then, a dynamic expert library is established to improve the adaptability of algorithm to the changing environment. And an inverse RL based on prior knowledge is presented to acquire reward functions. Experiments are conducted on a tightening assembly system and the results show that the tightening robot with the proposed model can inspect quality problems during the tightening process autonomously and make an adjustment decision based on the optimal policy that the agent calculates.

Augmented assembly (AA) is being gradually incorporated into manual assembly owing to its abundant instruction forms, high efficiency of information transmission, and robust interactivity. However, a lack of a unified description model for assembly information and irregular instruction design forms leads to difficulties in instruction authoring and generation, inconveniences for data management, and low system portability. To address these issues, this paper presents an information-integration and instruction-authoring method for AA systems. First, design guidelines for the AA instructions are established with the aim of minimizing the user's cognitive load. After that, the designing of visual elements is performed based on the design guidelines, and the information model of the assembly information and instructions is built using Unified Modeling Language. Based on Extensible Markup Language files and Unity3D engine, a standard and rapid development process is presented for deploying the AA system to Hololens2 devices. The usability and replicability of the designed system are demonstrated, and a user study is carried out. The user's task performance is evaluated using completion time and errors as metrics. The NASA-task load index scale is used to estimate the user's workload, and the Likert scale reflects the user's subjective perception of the system and instructions. The results show that the proposed AA instructions can effectively improve the user's task performance; reduce their workload, especially the cognitive load; and improve their assembly experience.