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: 2132847
• 负责人: Dorsa Sadigh
• 金额: $ 50.85万
• 依托单位: Stanford University
• 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=2132847&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.

该项目的目标是开发护理机器人，为行动不便的人提供日常生活活动（ADL）的长期帮助。尽管在受控环境中的可持续解决方案方面取得了巨大进步，但机器人还远未准备好在真实家庭环境中作为长期护理解决方案采用。关键因素可能是在非结构化环境中需要动态身体和社交互动的任务中过度依赖完全自主，以及缺乏个性化帮助。基于机器人需要优化身体和社交互动，为 ADL 提供高效、安全和个性化的帮助这一中心原则，这项工作将重点开发机器人辅助喂养，作为患有上肢障碍的人的长期护理解决方案。在非结构化的真实家庭环境中出现肢体残疾。成功的喂养包括咬合获取（即拿起食物）和咬合转移（即将其移入嘴中）。该项目开发了将这些活动整合起来的方法，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制定适应一系列人类和环境因素的人机循环控制策略。意识到在非结构化和动态环境中完全自主可能具有挑战性，这些方法将利用专家的人类反馈，同时最大限度地减少认知负荷，并将它们与自主智能地交织在一起，以达成长期护理解决方案。这项工作将对世界上数百万运动障碍患者的生活、健康和舒适产生直接影响。制定在每一步都考虑人类参与并通过多种方式从他们的反馈中学习的政策将对许多其他人机交互领域产生影响，包括但不限于辅助远程操作。该项目将推进最先进的技术从技术和算法的角度来看机器人技术。首先，将开发新颖的咬合采集算法，能够在非结构化环境中拾取可变形物体。其次，将开发咬转移算法，当将食物转移到人的嘴里时，该算法可以从机器人或器具上提供的物理反馈进行学习。最后，研究团队将开发主动和自适应算法，利用其他数据源，例如比较或语言指令，以智能地改进采集和传输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——来更好地操纵和转移食物。辅助采集和传输算法将通过人体研究进行广泛评估。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实施将促进辅助喂养之外的机器人技术的发展，特别是家庭环境中的其他 ADL 或工具性 ADL (IADL)，例如准备饭菜、烹饪和家务劳动。该奖项反映了 NSF 的法定使命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，并被认为值得支持。

项目成果

Balancing Efficiency and Comfort in Robot-Assisted Bite Transfer

平衡机器人辅助咬合转移的效率和舒适度

• 发表时间: 2022-01
• 期刊: International Conference on Robotics and Automation (ICRA
• 作者: Suneel Belkhale; Ethan Kroll
• 通讯作者: Ethan Kroll

Eliciting Compatible Demonstrations for Multi-Human Imitation Learning

引出多人模仿学习的兼容演示

• 发表时间: 2022-01
• 期刊: Proceedings of the 6th Conference on Robot Learning (CoRL
• 作者: Kanishk Gandhi; Siddharth Karamcheti
• 通讯作者: Siddharth Karamcheti

Learning Sequential Acquisition Policies for Robot-Assisted Feeding

学习机器人辅助喂养的顺序采集策略

• DOI: 10.48550/arxiv.2309.05197
• 发表时间: 2023-09-11
• 作者: Priya Sundaresan;Jiajun Wu;Dorsa Sadigh
• 通讯作者: Dorsa Sadigh

In-Mouth Robotic Bite Transfer with Visual and Haptic Sensing

具有视觉和触觉感应的口内机器人咬合转移

• 发表时间: 2023-05
• 期刊: International Conference on Robotics and Automation (ICRA
• 作者: Shaikewitz, Lorenzo;Wu, Yilin;Belkhale, Suneel;Grannen, Jennifer;Sundaresan, Priya;Sadigh, Dorsa
• 通讯作者: Sadigh, Dorsa

KITE: Keypoint-Conditioned Policies for Semantic Manipulation

KITE：用于语义操作的关键点条件策略

• DOI: 10.48550/arxiv.2306.16605
• 发表时间: 2023-06-29
• 期刊: 2022 International Conference on Robotics and Automation (ICRA)
• 作者: Priya Sundaresan;Suneel Belkhale;Dorsa Sadigh;J. Bohg
• 通讯作者: J. Bohg