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

RI-Large：认知助理的活动学习和识别

• 批准号: 1012017
• 负责人: Henry Kautz
• 金额: $ 75万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2014-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1012017&HistoricalAwards=false
• 关键词: RI; Large; Activity; Learning; Recognition

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.

该项目解决了推进认知辅助系统最先进水平的一个关键问题，该系统可以与人类自然交互，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，还可以帮助所有执行不熟悉的复杂任务的人。该研究重点关注适合实际实验的日常生活结构化活动，例如准备膳食和其他厨房活动。具体而言，该研究的核心焦点是活动识别，即可以识别一个人在执行任务时执行的目标和个人行为的系统。这项工作的关键创新在于 1）活动模型是通过直观的自然演示从用户那里学习的，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，当前的实践需要研究人员监督下的专门培训，并且不支持对模型的推理。这一进步是通过集成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。这项工作朝着构建实用且灵活的家庭自动化助理的目标迈出了重要一步。