In most applications, robots need to adapt to new environments and be multi-functional without forgetting previous information. This requirement gains further importance in real-world scenarios where robots operate in coexistence with humans. In these complex environments, human actions inevitably lead to changes, requiring robots to adapt accordingly. To effectively address these dynamics, the concept of continual learning proves essential. It not only enables learning models to integrate new knowledge while preserving existing information but also facilitates the acquisition of insights from diverse contexts. This aspect is particularly relevant to the issue of context-switching, where robots must navigate and adapt to changing situational dynamics. Our approach introduces a novel approach to effectively tackle the problem of context drifts by designing a Streaming Graph Neural Network that incorporates both regularization and rehearsal techniques. Our Continual\_GTM model enables us to retain previous knowledge from different contexts, and it is more effective than traditional fine-tuning approaches. We evaluated the efficacy of Continual\_GTM in predicting human routines within household environments, leveraging spatio-temporal object dynamics across diverse scenarios.

翻译：在大多数应用中，机器人需要适应新环境并具备多功能性，同时不遗忘先前信息。这一要求在机器人与人类共存的现实场景中尤为重要。在这些复杂环境中，人类行为不可避免地引发环境变化，要求机器人作出相应调整。为有效应对这些动态变化，持续学习的概念被证明至关重要。它不仅使学习模型能够整合新知识并保留现有信息，还能促进从多样化情境中获取洞察。这一特性与情境切换问题尤为相关，即机器人必须在变化的情境动态中进行导航与适应。本研究提出一种创新方法，通过设计融合正则化与回放技术的流式图神经网络，有效应对环境漂移问题。我们的Continual\_GTM模型能够保留来自不同情境的先前知识，其效果优于传统的微调方法。我们通过利用多样化场景中的时空物体动态，评估了Continual\_GTM在家庭环境中预测人类行为规律的有效性。