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.

我们结合了持续学习、语义推理和交互式机器学习的思想，并通过人机交互开发了一种新型的交互式持续学习架构，用于在家庭环境中不断学习语义知识。我们将该架构与一个物理移动机器人集成，并在实验室环境中进行了两个月的广泛系统评估。结果表明，我们的架构能够使物理机器人能够根据用户提供的有限数据不断适应环境的变化，并利用所学知识执行物体获取任务。

家庭服务机器人的长期个性化交互式连续学习架构

Interactive Continual Learning Architecture for Long-Term  Personalization of Home Service Robots

This paper presents a synthetic multimodal dataset of daily activities that
fuses video data from a 3D virtual space simulator with knowledge graphs
depicting the spatiotemporal context of the activities. The dataset is
developed for the Knowledge Graph Reasoning Challenge for Social Issues
(KGRC4SI), which focuses on identifying and addressing hazardous situations in
the home environment. The dataset is available to the public as a valuable
resource for researchers and practitioners developing innovative solutions
recognizing human behaviors to enhance safety and well-being in

本研究提供了一个合成的多模态数据集，通过融合来自 3D 虚拟空间模拟器的视频数据与描绘活动时空上下文的知识图谱，该数据集旨在用于社会问题的知识图谱推理挑战（KGRC4SI），重点是识别和解决家庭环境中的危险情况，该数据集对于研究人员和从业者开发创新解决方案，以识别人类行为以提升安全和福祉而言，可作为宝贵的资源对公众开放。