CAREER: Not all Collisions are Bad: Leveraging Physical Interactions towards User-Empowered Robotic Caregiving

构建安全、高效、有意义的机器人个体辅助系统，以实现长期生存，是机器人技术面临的一个重大挑战。2014年，超过四分之一的美国人患有残疾，2420万18岁或以上的人需要帮助进行日常生活活动，如喂食，洗澡，转移，穿衣，排便和走动。护理机器人有可能帮助增加或延长用户的独立性，同时减轻护理人员的负担。然而，目前几乎没有任何机器人能够为执行日常生活活动提供长期护理。其中一个主要原因是机器人可以推理执行这些活动所必需的物理接触的基本限制。因此，我们的关键见解是利用这种身体接触，而不是避免它们，并学习安全有效的身体互动，同时赋予用户权力。这个教师早期职业发展（CAREER）项目的重点是学习机器人控制政策，在机器人辅助喂养和床上洗澡的活动中利用身体接触，同时赋予用户自己决策的权力。该项目的目标是通过设计控制策略，利用各种多模态物理交互来安全有效地进行日常生活活动（ADL）。这也需要通过授权用户在不完全依赖完整但脆弱的自主权的情况下做出关键决策来解决非结构化环境中的感知和行动不确定性。辅助机器人和物理人机交互领域主要集中在使用针对感测优化的仔细控制的动作在末端执行器处的有意接触。这并没有捕捉到可能需要对偶然和/或分布式接触进行推理的物理接触的现实场景。该项目旨在弥补这一差距，并采取第一步，将实验室研究中的机器人物理部署转化为现实世界的部署。该研究计划分为三个重点，其中第一个重点是设计自主控制策略，使用多模态传感器输入，以实现安全有效的机器人物理控制。第二个推力使用户在循环中增强自主控制策略，并通过将用户包括在关键决策过程中来赋予用户权力。最后，第三个推力集中在设计控制策略，机器人的自主驾驶，可以适应用户不断变化的认知工作量。该项目的重点是机器人辅助喂养和床上洗澡的活动，旨在评估这些方法与C1-C4脊髓损伤的个人在他们的真实的家庭对长期援助。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

Building safe, efficient, and meaningful robotic individual assistance for long-term caregiving is a grand challenge of robotics. More than a quarter of people living in the United States in 2014 had a disability, and 24.2 million people aged 18 or older required assistance with activities of daily living such as feeding, bathing, transferring, dressing, toileting, and ambulating. Caregiving robots have the potential to help increase or prolong user independence while reducing caregiver burden. However, there are hardly any caregiving robots that provide long-term care for performing activities of daily living, currently. One of the primary reasons is the fundamental limitation to which robots can reason about physical contact which is essential to perform these activities. Therefore, our key insight is to leverage this physical contact instead of avoiding them and to learn safe and efficient physical interactions while empowering the users. This Faculty Early Career Development (CAREER) project focuses on learning robot control policies that leverage physical contact during the activities of robot-assisted feeding and bed-bathing while empowering the users themselves with decision-making capabilities for assisting care recipients with Spinal Cord Injury.The goal of this project is to develop fundamental capabilities towards reshaping the field of robotic physical caregiving by designing control policies that leverage varied multimodal physical interactions to perform activities of daily living (ADL) safely and efficiently. This also entails addressing perception and action uncertainty in unstructured environments by empowering the users to make critical decisions without completely relying on full but fragile autonomy. The fields of assistive robotics and physical human-robot interaction have primarily focused on deliberate contact at the end-effector using carefully controlled actions that are optimized for sensing. This does not capture the realistic scenarios of physical caregiving that may require reasoning about incidental and/or distributed contacts. This project aims to cover this gap and takes the first steps towards translating laboratory research in robotic physical caregiving to real-world deployment. The research plan is organized into three thrusts where the first thrust focuses on designing autonomous control policies that use multimodal sensory inputs for safe and efficient robotic physical caregiving. The second thrust brings users in the loop to augment autonomous control policies and empowers the users by including them in the critical decision-making process. Finally, the third thrust focuses on designing control policies for robotic caregiving that can adapt to a user's changing cognitive workload. This project focuses on the activities of robot-assisted feeding and bed-bathing and aims to evaluate these methods with individuals with C1-C4 Spinal Cord Injury in their real homes towards long-term assistance.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.