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.

超过一百万患有身体残疾的美国成年人需要饮食帮助。机器人可以通过伸手、捡起食物并将其送至用户嘴里来帮助这些人。如今的辅助机器人使用传统器具（如叉子）来拾取食物，但当用户想要拾取小、滑且形状不规则的食物时，这些器具就显得不够了。另一方面，软机器人抓手是抓取这些食物的有力候选者——但软抓手可能难以抓住大而重的物品（例如一杯水）。该奖项旨在通过将柔软的可调粘合剂和刚性的并行机构组合成一个单一的刚软机器人抓手，来发展对物体操纵的新的基本理解。研究人员团队将探索人类操作员如何使用这些夹具，以及这些夹具如何向人类学习，以自动化伸手和抓取物品的过程。由此产生的刚软夹持器范式将扩大机器人可以拾取的食物和其他物体的范围。除了通过辅助饮食提高生活质量外，这项技术还应用于制造工厂、食品加工和水果采收，它可以使机械臂抓取不同质地、大小和形状的物体。为了激励和培训 K-12 学生未来的工程职业生涯，该团队将举办现场和远程机器人演示，让学生控制机器人手臂和刚软抓手。该项目引入了用于对象操纵的物理和算法形式，将刚性和软机器人抓手沿连续范围统一起来。关键的见解是，设计人员可以利用活性粘合剂的最新进展，在刚性夹具上涂上一系列软材料，而不是开发刚性或柔软的夹具。研究人员团队将：i）描述最佳情况的机器人和日常人类如何使用刚性、软性和刚软夹子，ii）从人类输入中学习，以使用刚软夹子自主且稳健地拾取新物品，以及 iii）在物理方程下统一刚性和软性夹子的范围，该方程预测夹力作为夹子设计、物体标准和人类控制模式的函数。基于物理的粘附形式与学习操作员模型的结合将为粘附现象和用户输入如何增强抓取能力提供新的见解。这些贡献将通过残疾和非残疾参与者远程操作机器人手臂和刚软抓手的用户研究进行评估。该项目得到跨部门机器人基础研究计划的支持，该计划由工程理事会 (ENG) 和计算机与信息科学与工程理事会 (CISE) 共同管理和资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，认为值得支持。 智力价值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