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

该项目的目标是开发能够为行动不便的人提供长期日常生活活动(ADL)帮助的照看机器人。尽管在受控环境中的可持续解决方案方面取得了长足的进步，但机器人还远未准备好在真实的家庭环境中作为长期护理解决方案被采用。关键因素可能是过度依赖需要在非结构化环境中进行动态的身体和社会互动的任务的完全自主权，以及缺乏个性化的协助。基于机器人需要优化身体和社会互动以为ADL提供高效、安全和个性化帮助的中心宗旨，这项工作将专注于开发机器人辅助喂养，作为在非结构化的真实家庭环境中为上肢残疾人士提供长期护理解决方案。成功的喂食包括咬合获得(即拿起食物)和咬合转移(即将食物移入嘴里)。该项目制定了将这些活动结合起来的方法，以开发智能和个性化的机器人辅助喂养系统。这些模型利用多模式反馈来开发适应一系列人类和环境因素的人在环控制策略。意识到完全自主在非结构化和动态环境中可能是具有挑战性的，这些方法将利用专家的人类反馈，同时将认知负荷降至最低，并将它们与自主智能地交织在一起，以达成长期的照看解决方案。这项工作将直接影响世界上数百万运动障碍患者的生活、健康和舒适度。制定政策，在每一步都考虑人类，并通过多种方式学习他们的反馈，将对许多其他人-机器人交互领域产生影响，包括但不限于辅助遥操作。该项目将从技术和算法角度推动机器人技术的发展。首先，将开发能够在非结构化环境中拾取可变形物体的新型咬合获取算法。其次，将开发咬合转移算法，当人类嘴里转移食物时，可以从机器人或器皿上提供的物理反馈中学习。最后，研究团队将开发主动和自适应的算法，利用其他数据来源，如比较或语言指令，智能地改进获取和传输算法，并使喂养体验个性化。这项工作利用了从多模式人类反馈中学习的想法-特别是通过拥抱物理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体受试者研究进行广泛评估。这些算法将在实验室的多个高自由度机器人平台上实施。计划中的用户研究和低水平的实施将推动机器人在辅助喂养之外的状态，特别是在家庭环境中的其他ADL或工具性ADL(IADL)，如做饭、烹饪和家务。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.