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

点击翻译按钮获取中文摘要

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.