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

2010年，近5670万（18.7%）的美国非机构人口患有残疾。其中，约1230万人需要一项或多项日常生活活动（adl）的帮助，如进食、洗澡或穿衣。机器人有潜力帮助完成这些日常生活活动，但每个用户都是不同的，他们有不同的需求和偏好。对于长期护理来说，这种辅助系统能够适应不同的情况和用户偏好是至关重要的。这个项目专注于喂食活动，并基于这样一个核心原则：通过利用用户反馈和以前喂食尝试的背景，机器人应该能够在线学习如何适应新的食物、用户偏好和环境。通过改善独立生活的机会，该项目的成果可以对全世界数百万人产生积极影响。这项研究的长期前景是让机器人在社会中能够在现实家庭中混乱、复杂和动态的人类环境中无缝、流畅地执行复杂的操作任务。该项目使用基于上下文强盗的通用框架形式化机器人辅助喂养，该框架允许直接在线优化用户偏好。应用于获取和转移食物的在线上下文强盗框架为利用用户反馈来基准，学习和开发自然用餐体验的方法提供了基础，并探索不同的上下文来推广咬习。这些模型通过用户反馈直接优化用户体验，并通过智能使用嵌入式传感适应一系列社会和环境因素。该项目探索的解决方案能够平衡高质量但昂贵的专家援助和以共享自治系统的形式学习的更便宜的解决方案之间的权衡。关键问题包括用户在时间上和人种学上的偏好多样性，在整个学习过程中为体验进行设计，以及处理高维上下文信息。实际结果将是一个智能辅助喂食机器人，其性能可以推广到不同的活动，并适应用户的偏好。一种基于用户反馈和丰富传感器信息的智能辅助喂食机器人将推动复杂用户体验和社会环境集成到一个连贯的学习机器人系统中。上下文强盗是一种高度优化的多假设检验的泛化，在人类-机器人和人类-人工智能系统中具有广泛的潜力，可以有效地实时适应特定的用户需求。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.