NRI/Collaborative Research: Robot-Assisted Feeding: Towards Efficient, Safe, and Personalized Caregiving Robots

NRI/合作研究：机器人辅助喂养：迈向高效、安全和个性化的护理机器人

• 批准号: 2132848
• 负责人: Siddhartha Srinivasa
• 金额: $ 50.5万
• 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: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2132848&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Robot; Assisted

The goal of this project is to develop caregiving robots that can provide long-term assistance with activities of daily living (ADLs) to people with mobility limitations. Despite great strides taken towards sustainable solutions in controlled environments, robots are far from ready for adoption in real home environments as long-term caregiving solutions. Key factors could be the over-reliance on full autonomy in tasks that require dynamic physical and social interactions in unstructured environments as well as the lack of personalized assistance. Based on the central tenet that robots need to optimize both physical and social interactions to provide efficient, safe, and personalized assistance for ADLs, this work will focus on developing robot-assisted feeding as a long-term caregiving solution for a person with upper-extremity disability in an unstructured, real-home environment. Successful feeding consists of bite acquisition (i.e., picking up a food item) and bite transfer (i.e., moving it into the mouth). This project develops methods to integrate these activities towards the development of an intelligent and personalized robot-assisted feeding system. The models leverage multimodal feedback to develop human-in-the-loop control policies that adapt to a range of human and environmental factors. Realizing that full autonomy can be challenging in unstructured and dynamic environments, the methods will leverage expert human feedback while minimizing the cognitive load and interweave them intelligently with autonomy to arrive at a long-term caregiving solution. This work will have a direct impact on the lives, health, and comfort of millions of people in the world who live with motor impairments. Developing policies that consider the human in the loop at every step and learn from their feedback through multiple modalities will have an impact on many other human-robot interaction domains including but not limited to assistive teleoperation.This project will advance the state of the art of robotics from both a technical and algorithmic perspective. First, novel bite acquisition algorithms will be developed that are capable of picking up deformable objects in unstructured settings. Second, bite-transfer algorithms will be developed that learn from physical feedback provided on the robot or on the utensil when transferring food inside of a person's mouth. Finally, the research team will develop active and adaptive algorithms that tap into other sources of data, such as comparisons or language instructions, to intelligently improve the acquisition and transfer algorithms and personalize the feeding experience. This work leverages the idea of learning from multimodal human feedback---specifically by embracing physical interactions rather than trying to avoid them---to better manipulate and transfer food. The assistive acquisition and transfer algorithms will be extensively evaluated through human subject studies. The algorithms will be implemented on multiple high-degree-of-freedom robotic platforms across labs. Planned user studies and low-level implementations will advance the state of robotics outside of assistive feeding, particularly towards other ADLs or Instrumental ADLs (IADLs) in home settings, such as meal-preparation, cooking, and housework.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.

该项目的目的是开发护理机器人，这些机器人可以为具有行动限制的人提供长期帮助（ADL）的活动（ADL）。尽管在受控环境中迈向可持续解决方案，但机器人远未准备好在实际家庭环境中作为长期护理解决方案采用。关键因素可能是在需要动态的身体和社交互动的任务中过度依赖自主权在非结构化环境中以及缺乏个性化的帮助。基于机器人需要优化物理和社交互动以为ADL提供高效，安全和个性化的援助的中心宗旨，这项工作将着重于开发机器人辅助的喂养，作为在非结构化的现实房屋环境中具有上高级残疾的长期护理解决方案。成功的喂养包括咬合（即拿起食品）和咬合转移（即将其移入口腔）组成。该项目开发了将这些活动集成到开发智能和个性化机器人辅助喂养系统的方法。这些模型利用多模式反馈来制定适合一系列人类和环境因素的人类在环境控制策略。意识到在非结构化和动态的环境中完全自治可能具有挑战性，这些方法将利用专家的反馈，同时最大程度地减少认知负担，并将其与自主权巧妙地交织在一起以达成长期护理解决方案。这项工作将直接影响世界上数以百万计的运动障碍的人的生活，健康和舒适。制定各个步骤中考虑人类的政策，并通过多种方式从反馈中学习反馈将对许多其他人类机器人互动领域产生影响。该项目将从技术和算法的角度来推动机器人技术的辅助性远程处理。首先，将开发出新颖的咬合采集算法，这些算法能够在非结构化设置中拾取可变形的对象。其次，将开发出咬合转移算法，这些算法从机器人或餐具上提供的物理反馈中学习时，将食物转移到一个人的嘴里。最后，研究团队将开发积极和自适应的算法，这些算法利用其他数据来源（例如比较或语言说明），以明智地改善获取和转移算法并个性化喂养体验。这项工作利用了从多模式人类反馈中学习的想法 - 特别是通过拥抱身体互动而不是试图避免它们 - 更好地操纵和转移食物。辅助获取和转移算法将通过人类学科研究进行广泛评估。该算法将在各个实验室的多个高度自由度的机器人平台上实现。 Planned user studies and low-level implementations will advance the state of robotics outside of assistive feeding, particularly towards other ADLs or Instrumental ADLs (IADLs) in home settings, such as meal-preparation, cooking, and housework.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.