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

职业生涯：并非所有碰撞都是坏事：利用物理交互实现用户授权的机器人护理

基本信息

批准号：
2238792
负责人：
Tapomayukh Bhattacharjee
金额：
$ 55.79万
依托单位：
Cornell University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-03-01 至 2028-02-29
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238792&HistoricalAwards=false
关键词：
CAREER Collisions Bad Leveraging Physical

项目摘要

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.

为长期护理构建安全、高效、有意义的机器人个人辅助是机器人技术面临的巨大挑战。 2014 年，超过四分之一的美国人患有残疾，2420 万名 18 岁或以上的人需要协助进行日常生活活动，如进食、洗澡、转移、穿衣、如厕和行走。护理机器人有潜力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，目前几乎没有任何护理机器人能够为日常生活活动提供长期护理。主要原因之一是机器人推理身体接触的基本限制，而身体接触对于执行这些活动至关重要。因此，我们的主要见解是利用这种身体接触而不是避免它们，并在赋予用户权力的同时学习安全有效的身体互动。该教师早期职业发展（CAREER）项目的重点是学习机器人控制策略，这些策略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能力，以帮助患有脊髓损伤的护理人员。该项目的目标是通过设计控制策略，利用各种多模式物理交互来安全有效地执行日常生活活动（ADL），从而开发重塑机器人物理护理领域的基本能力。这还需要通过授权用户在不完全依赖完全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末端执行器上的故意接触，使用针对传感进行优化的精心控制的动作。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身体护理的现实场景。该项目旨在弥补这一差距，并迈出将机器人物理护理实验室研究转化为现实世界部署的第一步。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，使用多模态感官输入来实现安全高效的机器人物理护理。第二个推动力使用户参与到增强自主控制策略的循环中，并通过将用户纳入关键决策过程来赋予用户权力。最后，第三个重点是设计机器人护理的控制策略，以适应用户不断变化的认知工作量。该项目重点关注机器人辅助喂养和床上洗澡的活动，旨在对 C1-C4 脊髓损伤患者在真实家中评估这些方法，以实现长期援助。该奖项反映了 NSF 的法定使命，并被视为值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。