Unifying Rigid and Soft Grippers for Assistive Eating

统一刚性和软质夹具以辅助进食

• 批准号: 2205241
• 负责人: Dylan Losey
• 金额: $ 62.48万
• 依托单位: Virginia Polytechnic Institute and State 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: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2205241&HistoricalAwards=false
• 关键词: Unifying; Rigid; Soft; Grippers; Assistive

Over one million American adults living with physical disabilities need help eating. Robots can assist these people by reaching for, picking up, and carrying bites of food to the user's mouth. Today's assistive robots pick up foods using traditional utensils (like forks), but these utensils fall short when the user wants to pick up small, slippery, and irregularly shaped foods. On the other hand, soft robotic grippers are strong candidates for grasping these foods --- but soft grippers may struggle to hold large, heavier items (such as a glass of water). This award aims to develop new fundamental understanding of object manipulation by combining soft, tunable adhesives and rigid, parallel mechanisms into a single rigid-soft robotic gripper. The team of researchers will explore how human operators use these grippers, and how these grippers can learn from humans to automate the process of reaching for and grasping items. The resulting rigid-soft gripper paradigm will expand the range of foods and other objects a robot can pick up. In addition to improving quality of life through assistive eating, this technology has applications in manufacturing factories, food processing, and fruit harvesting, where it can enable robotic arms to grasp objects of diverse textures, sizes, and shapes. To inspire and train K-12 students for future careers in engineering, the team will host live and remote robotic demonstrations where students control a robot arm and rigid-soft gripper.This project introduces a physics and algorithmic formalism for object manipulation that unifies rigid and soft robotic grippers along a continuous spectrum. The key insight is that --- instead of developing grippers that are either rigid or soft --- designers can leverage recent advances in active adhesives to coat rigid grippers with an array of soft materials. The team of investigators will: i) Characterize how best-case robots and everyday humans utilize rigid, soft, and rigid-soft grippers, ii) Learn from human inputs to autonomously and robustly pick up new items with rigid-soft grippers, and iii) Unify the spectrum of rigid and soft grippers under a physics equation that predicts gripping forces as a function of gripper design, object criteria, and human control patterns. The combination of a physics-based adhesion formalism with learned operator models will provide new insights into how adhesion phenomena and user inputs can enhance gripping capacity. The contributions will be evaluated through user studies where disabled and non-disabled participants teleoperate a robot arm and rigid-soft gripper.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.

超过一百万的美国身体残疾的成年人需要帮助饮食。机器人可以通过伸手去拿，捡起食物来帮助这些人。当今的辅助机器人使用传统的餐具（如叉子）捡起食物，但是当用户想拿起小，湿滑且形状不规则的食物时，这些餐具会掉落。另一方面，柔软的机器人抓手是抓住这些食物的强大候选者 - 但是柔软的抓手可能难以容易容纳大型，更重的物品（例如一杯水）。该奖项旨在通过将柔软，可调的粘合剂和刚性的平行机制结合到单个刚性柔软的机器人抓手中来发展对物体操纵的新基本理解。研究人员团队将探讨人类操作员如何使用这些抓手，以及这些抓手如何向人类学习以自动化和抓住物品的过程。由此产生的僵硬的抓地力范式将扩大机器人可以拾取的食物和其他物体的范围。除了通过辅助饮食改善生活质量外，这项技术还在制造工厂，食品加工和水果收获中应用，它可以使机器人手臂能够掌握各种质地，尺寸和形状的物体。为了激发和培训K-12学生的工程职业，团队将举办现场和远程机器人演示，学生控制机器人手臂和僵硬的握手。该项目介绍了物理和算法形式主义，以统一刚性刚性和软机器人的夹具，并沿着连续的光谱统一僵硬的机器人。关键的见解是 - - - - 而不是开发刚性或柔软的抓地力 - 设计师可以利用活跃胶粘剂的最新进展来涂上一系列柔软材料的刚性抓地力。 The team of investigators will: i) Characterize how best-case robots and everyday humans utilize rigid, soft, and rigid-soft grippers, ii) Learn from human inputs to autonomously and robustly pick up new items with rigid-soft grippers, and iii) Unify the spectrum of rigid and soft grippers under a physics equation that predicts gripping forces as a function of gripper design, object criteria, and human control patterns.基于物理学的粘附形式主义与学习的操作员模型的结合将提供有关粘附现象和用户输入如何增强吸引能力的新见解。该贡献将通过用户研究评估，在该用户研究中，残疾人和非障碍参与者都会对机器人组和僵硬的抓地力进行远程处理。本项目得到了机器人计划中的跨导向基础研究的支持，该项目由工程（ENG）和计算机和信息科学和工程（CISE）进行了反映，并以反映了DEDERS的指示，并由计算机和信息奖授课，共同管理和资助。智力优点和更广泛的影响审查标准。

项目成果

RISO: Combining Rigid Grippers with Soft Switchable Adhesives

• DOI: 10.1109/robosoft55895.2023.10122030
• 发表时间: 2022-10
• 期刊: 2023 IEEE International Conference on Soft Robotics (RoboSoft)
• 作者: Shaunak A. Mehta;Yeunhee Kim;Joshua Hoegerman;Michael D. Bartlett;Dylan P. Losey