Unifying Rigid and Soft Grippers for Assistive Eating

美国有100多万身体残疾的成年人需要帮助进食。机器人可以通过伸手、捡起食物并将食物送到使用者的嘴里来帮助这些人。今天的辅助机器人使用传统的餐具(如叉子)来挑选食物，但当用户想要捡起小的、滑的和形状不规则的食物时，这些餐具就不够用了。另一方面，柔软的机器人抓手是抓取这些食物的有力人选-但柔软的抓手可能难以抓取较大、较重的物品(如一杯水)。该奖项旨在通过将柔软、可调的粘合剂和刚性、并联机构结合到单个刚柔机器人抓取器中，发展对对象操纵的新的基本理解。研究小组将探索人类操作员如何使用这些抓取器，以及这些抓取器如何向人类学习，使伸手和抓取物品的过程自动化。由此产生的刚柔夹爪范例将扩大机器人可以拿起的食物和其他物体的范围。除了通过辅助进食提高生活质量外，这项技术还应用于制造工厂、食品加工和水果收获，在这些领域，它可以使机械臂抓住不同纹理、大小和形状的物体。为了激励和培训K-12的学生在未来的工程职业生涯中，该团队将举办现场和远程机器人演示，学生们控制机器人手臂和刚柔夹持器。该项目引入了对象操纵的物理和算法形式主义，将刚性和软性机器人夹持器在连续光谱上统一起来。关键的见解是-不是开发硬质或软质的夹持器-设计师可以利用活性粘合剂的最新进展，在硬质夹持器上涂上一系列软材料。研究团队将：i)描述最好的情况下机器人和日常人类如何利用刚柔并济的抓取器；ii)从人类的输入中学习，使用刚柔并济的抓取器自主而有力地拾取新物品；iii)在物理方程下统一刚性和柔软性抓取器的频谱，该方程预测抓取力作为抓取器设计、物体标准和人类控制模式的函数。将基于物理的粘着形式论与学习的操作者模型相结合，将为粘着现象和用户输入如何提高抓取能力提供新的见解。这些贡献将通过用户研究来评估，在用户研究中，残疾和非残疾参与者远程操作机器人手臂和刚柔夹爪。该项目由跨部门机器人基础研究计划支持，该计划由工程总监(ENG)和计算机和信息科学与工程(CEISE)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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.