Unifying Rigid and Soft Grippers for Assistive Eating

超过100万美国成年人的身体残疾需要帮助饮食。机器人可以帮助这些人伸手去拿、捡起食物，并把咬过的食物送到使用者的嘴里。今天的辅助机器人使用传统的餐具（如叉子）来拿起食物，但当用户想要拿起小的，光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者-但是柔软的抓手可能很难抓住大的、重的物品（比如一杯水）。该奖项旨在通过将柔软的、可调的粘合剂和刚性的、平行的机构结合到单个软硬机器人夹持器中，来发展对物体操作的新的基本理解。研究人员将探索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，以自动化抓取物品的过程。由此产生的软硬夹持器范例将扩大机器人可以拾取的食物和其他物体的范围。除了通过辅助进食提高生活质量外，这项技术还可以应用于制造工厂，食品加工和水果收获，使机器人手臂能够抓住不同质地，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软硬夹持器。该项目介绍了物体操作的物理和算法形式主义，将刚性和柔性机器人夹持器沿着连续频谱统一起来。关键的见解是-而不是开发刚性或柔软的夹具-设计师可以利用活性粘合剂的最新进展，用一系列柔软材料涂覆刚性夹具。研究小组将：i）描述最佳情况下机器人和日常人类如何利用刚性，柔性和刚性-柔性抓手，ii）从人类输入中学习，以使用刚性-柔性抓手自主和稳健地拾取新物品，iii）在物理方程下统一刚性和柔性抓手的频谱，该物理方程预测抓手设计，对象标准和人类控制模式的函数。基于物理学的粘附形式主义与学习操作员模型的结合将为粘附现象和用户输入如何提高抓握能力提供新的见解。这些贡献将通过用户研究进行评估，其中残疾人和非残疾人参与者远程操作机器人手臂和软硬夹持器。该项目得到了机器人项目跨部门基础研究的支持，由工程局（ENG）和计算机与信息科学与工程局（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.