For robots to perform assistive tasks in unstructured home environments, they must learn and reason on the semantic knowledge of the environments. Despite a resurgence in the development of semantic reasoning architectures, these methods assume that all the training data is available a priori. However, each user's environment is unique and can continue to change over time, which makes these methods unsuitable for personalized home service robots. Although research in continual learning develops methods that can learn and adapt over time, most of these methods are tested in the narrow context of object classification on static image datasets. In this paper, we combine ideas from continual learning, semantic reasoning, and interactive machine learning literature and develop a novel interactive continual learning architecture for continual learning of semantic knowledge in a home environment through human-robot interaction. The architecture builds on core cognitive principles of learning and memory for efficient and real-time learning of new knowledge from humans. We integrate our architecture with a physical mobile manipulator robot and perform extensive system evaluations in a laboratory environment over two months. Our results demonstrate the effectiveness of our architecture to allow a physical robot to continually adapt to the changes in the environment from limited data provided by the users (experimenters), and use the learned knowledge to perform object fetching tasks.

翻译：为使机器人在非结构化的家庭环境中执行辅助任务，它们必须学习并推理环境的语义知识。尽管语义推理架构的研究重新兴起，但这些方法假设所有训练数据预先可用。然而，每个用户的独特环境可能随时间持续变化，这使得这些方法不适用于个性化的家庭服务机器人。尽管持续学习领域的研究已开发出能随时间学习与适应的算法，但其中大多数方法仅在静态图像数据集上的物体分类这一狭窄场景下进行测试。本文融合持续学习、语义推理与人机交互文献的思想，提出一种新颖的交互式持续学习架构，通过人机交互实现家庭环境中语义知识的持续学习。该架构基于学习与记忆的核心认知原理，实现从人类高效实时地学习新知识。我们将该架构集成到实体移动机械臂机器人中，并在实验室环境中进行了为期两个月的系统性系统评估。实验结果证明了该架构的有效性：使实体机器人能利用用户（实验者）提供的有限数据持续适应环境变化，并运用所学知识执行物体取物任务。