Title: Deep Generative and Discriminative Digital Twin endowed with Variational Autoencoder for Unsupervised Predictive Thermal Condition Monitoring of Physical Robots in Industry 6.0 and Society 6.0 Show Chinese title

Title: 基于变分自编码器的深度生成和判别数字孪生，用于工业6.0和社会6.0中物理机器人的无监督预测热状态监测

Abstract: Robots are unrelentingly used to achieve operational efficiency in Industry 4.0 along with symbiotic and sustainable assistance for the work-force in Industry 5.0. As resilience, robustness, and well-being are required in anti-fragile manufacturing and human-centric societal tasks, an autonomous anticipation and adaption to thermal saturation and burns due to motors overheating become instrumental for human safety and robot availability. Robots are thereby expected to self-sustain their performance and deliver user experience, in addition to communicating their capability to other agents in advance to ensure fully automated thermally feasible tasks, and prolong their lifetime without human intervention. However, the traditional robot shutdown, when facing an imminent thermal saturation, inhibits productivity in factories and comfort in the society, while cooling strategies are hard to implement after the robot acquisition. In this work, smart digital twins endowed with generative AI, i.e., variational autoencoders, are leveraged to manage thermally anomalous and generate uncritical robot states. The notion of thermal difficulty is derived from the reconstruction error of variational autoencoders. A robot can use this score to predict, anticipate, and share the thermal feasibility of desired motion profiles to meet requirements from emerging applications in Industry 6.0 and Society 6.0. Show Chinese abstract

Abstract: 机器人在工业4.0中被不断使用以实现操作效率，并在工业5.0中为劳动力提供共生和可持续的帮助。 由于抗脆弱制造和以人为中心的社会任务需要韧性、鲁棒性和幸福感，因此机器人对电机过热导致的热饱和和烧伤进行自主预测和适应变得至关重要，这对于人类安全和机器人可用性非常重要。 因此，机器人除了与其他代理提前沟通其能力以确保完全自动化的热可行性任务并延长其寿命而无需人工干预外，还应能够自我维持其性能并提供用户体验。 然而，当面临即将发生的热饱和时，传统的机器人关机会抑制工厂的生产力和社会的舒适度，而冷却策略在机器人购买后很难实施。 在本研究中，配备了生成式人工智能（即变分自编码器）的智能数字孪生被用来管理热异常并生成无风险的机器人状态。 热难度的概念来源于变分自编码器的重建误差。 机器人可以使用这个评分来预测、预见并分享所需运动轨迹的热可行性，以满足工业6.0和社会6.0中新兴应用的要求。