RI-Large: Activity Learning and Recognition for a Cognitive Assistant

该项目解决了一个关键问题，即推进认知辅助系统的最新技术，该系统可以与人类自然互动，以帮助他们更有效地执行日常任务。这样的系统不仅可以帮助有认知障碍的人，还可以帮助所有人完成他们不熟悉的复杂任务。这项研究的重点是日常生活中结构化的活动，这些活动可以用于实际实验，比如准备饭菜和其他厨房活动。具体来说，研究的核心焦点是活动识别，即能够识别一个人在完成任务时正在执行的目标和个人行为的系统。这项工作的关键创新是1)通过直观的自然演示从用户那里学习活动模型，以及2)系统能够对活动模型进行推理，以概括和适应它们。相比之下，目前的实践需要由研究人员监督的专门培训，并且不支持对模型进行推理。这一进步是通过整合通常单独研究的功能来实现的，包括活动识别、知识表示和推理、自然语言理解和机器学习。这项工作为建立实用和灵活的家庭自动化助手的目标迈出了重要的一步。

This project addresses a key problem in advancing the state of the art in cognitive assistant systems that can interact naturally with humans in order to help them perform everyday tasks more effectively. Such a system would help not only people with cognitive disabilities but all individuals as they perform complex tasks they are unfamiliar with. The research focuses on structured activities of daily living that lend themselves to practical experimentation, such as meal preparation and other kitchen activities.Specifically, the core focus of the research is activity recognition, i.e., systems that can identify the goals and individual actions a person is performing as they work on a task. Key innovations of this work are 1) that the activity models are learned from the user via intuitive natural demonstration, and 2) that the system is able to reason over activity models to generalize and adapt them. In contrast, current practice requires specialized training supervised by the researchers and supports no reasoning over the models. This advance is accomplished by integrating capabilities that are typically studied separately, including activity recognition, knowledge representation and reasoning, natural language understanding and machine learning. The work addresses a significant step towards the goal of building practical and flexible in-home automated assistants.