CHS: Small: Towards Usability in Robotic Assistance: A Formalism for Robot-Assisted Feeding while Adjusting to User Preferences

CHS：小：迈向机器人辅助的可用性：机器人辅助喂养的形式主义，同时根据用户偏好进行调整

• 批准号: 2007011
• 负责人: Siddhartha Srinivasa
• 金额: $ 49.51万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007011&HistoricalAwards=false
• 关键词: CHS; Small; Towards; Usability; Robotic

Nearly 56.7 million (18.7%) of the non-institutionalized US population had a disability in 2010. Among them, about 12.3 million needed assistance with one or more activities of daily living (ADLs), such as feeding, bathing, or dressing. Robots have the potential to help with these activities of daily living but every user is different and they have diverse needs and preferences. For long-term care, it is essential that such an assistive system can adapt to diverse situations and user preferences. This project focuses on the feeding activity and is based on this central tenet that by leveraging user feedback and contexts from previous feeding attempts, a robot should be able to learn online how to adapt to new food items, user preferences, and environments. Through improved access to independent living, the results of this project can positively impact millions of people worldwide. The long-term promise of this research is to have robots in society that can seamlessly and fluently perform complex manipulation tasks in cluttered, complex, and dynamic human environments in real homes.This project formalizes robot-assisted feeding using a general framework based on contextual bandits that allows directly optimizing for user preferences online. The online contextual bandit framework applied to acquiring and transferring food items provides the foundation to leverage user feedback to benchmark, learn, and develop methods for a natural dining experience, and exploring different contexts for generalizing bite acquisition. The models directly optimize for the user experience through user feedback, and adapt to a range of social and environmental factors with the intelligent use of embedded sensing. The project explores solutions which balance the trade-off between high quality but costly expert assistance and cheaper learned solutions in the form of a shared-autonomy system. Critical issues include the diversity of user preferences both temporally and ethnographically, designing for the experience across the entire learning procedure, and processing high-dimensional contextual information. The tangible result will be an intelligent assistive feeding robot whose performance can generalize to different activities and adapt to user preferences. An intelligent assistive feeding robot that relies on user feedback and rich sensor information will advance integrating complex user experiences and social environments into a coherent learning robotic system. Contextual bandits, a highly optimized generalization of multiple hypothesis testing, have broad potential in human-robotic, and human-AI systems in general to efficiently adapt to specific user needs in real time.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2010年，非机构化的美国人口中有近5670万（18.7％）患有残疾。其中约有1,230万所需要的日常生活活动（ADL）（例如喂养，沐浴或穿衣）需要帮助。机器人有可能帮助进行这些日常生活的活动，但是每个用户都不同，他们都有各种需求和偏好。为了长期护理，这种辅助系统必须适应各种情况和用户偏好。该项目的重点是喂养活动，并基于此中心宗旨，即通过利用用户反馈和以前的喂养尝试中的上下文，机器人应该能够在线学习如何适应新食品，用户偏好和环境。通过改善独立生活的机会，该项目的结果可以积极影响全球数百万人。这项研究的长期承诺是在社会上拥有机器人，可以在真实家庭中的混乱，复杂和动态的人类环境中无缝，流利地执行复杂的操纵任务。本项目将基于上下文宽图的一般框架正式化了基于上下文宽图的一般框架，从而可以直接在线优化用户偏好。用于获取和转移食品的在线上下文匪徒框架为利用用户反馈以基准，学习和开发自然用餐体验的方法为基础提供了基础，并探索了不同的上下文，以概括咬合咬合。这些模型通过用户反馈直接为用户体验优化，并通过智能使用嵌入式感应来适应一系列社交和环境因素。该项目探讨了解决方案，这些解决方案平衡了高质量但昂贵的专家援助和以共享自治系统形式更便宜的学习解决方案之间的权衡。关键问题包括在时间和民族志上的用户偏好多样性，在整个学习过程中为体验设计以及处理高维上下文信息。有形的结果将是一个智能的辅助喂养机器人，其性能可以推广到不同的活动并适应用户偏好。依靠用户反馈和丰富的传感器信息的智能辅助喂养机器人将使复杂的用户体验和社交环境将其整合到一个连贯的学习机器人系统中。上下文匪徒是多种假设检验的高度优化的概括，在人类自动化和人类系统中具有广泛的潜力，通常可以在实时实时有效地适应特定的用户需求。该奖项反映了NSF的法定任务，并被视为值得通过基金会的知识分子优点和更广泛的影响来审查审查标准来通过评估来获得的支持。