Unifying Rigid and Soft Grippers for Assistive Eating

超过一百万的美国成年残疾人需要帮助进食。机器人可以帮助这些人伸手去拿起食物，并把食物送到用户的嘴里。今天的辅助机器人用传统的餐具（比如叉子）捡起食物，但是当用户想要捡起小的、滑的和不规则形状的食物时，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候选人，但柔软的抓手可能很难抓住大而重的东西（比如一杯水）。该奖项旨在通过将软的、可调的粘合剂和刚性的、平行的机构结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，以实现伸手和抓取物品的自动化过程。由此产生的刚软夹持器范例将扩大机器人可以拾取食物和其他物体的范围。除了通过辅助进食来提高生活质量外，这项技术还可以应用于制造工厂、食品加工和水果收获等领域，使机械臂能够抓取不同质地、大小和形状的物体。为了激励和培养K-12学生未来的工程职业，该团队将举办现场和远程机器人演示，学生们可以控制机器人手臂和软硬夹持器。该项目介绍了物体操作的物理和算法形式，将刚性和柔性机器人抓手沿着连续光谱统一起来。关键的观点是，设计师可以利用活性粘合剂的最新进展，在刚性夹持器上涂上一系列软材料，而不是开发刚性或柔软的夹持器。研究团队将：i)描述最佳情况下机器人和日常人类如何使用刚性，柔软和刚软夹持器，ii)从人类输入中学习，以自主和稳健地使用刚软夹持器拾取新物品，iii)在物理方程下统一刚性和软夹持器的范围，该方程预测夹持力作为夹持器设计，对象标准和人类控制模式的函数。基于物理的粘附形式与学习算子模型的结合将为粘附现象和用户输入如何增强抓握能力提供新的见解。贡献将通过用户研究进行评估，其中残疾和非残疾参与者远程操作机器人手臂和软硬夹持器。该项目由跨部门机器人基础研究项目支持，由工程（ENG）和计算机与信息科学与工程（CISE）联合管理和资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.