NRI: Design and Fabrication of Robot Hands for Dexterous Tasks

灵巧的操作是当今机器人技术的一大挑战。在家庭、太空、医疗应用、灾难场景和灵活制造中工作的机器人都需要灵巧性。这些环境中的机器人必须能够操纵、提升和处理物体，将它们作为工具使用，并在各种不确定的情况下胜任地将物体从一个安全的支撑点转移到另一个支撑点。这样的操作对机器人来说非常困难，并对它们的广泛采用构成了障碍。快速原型和数字制造的最新进展使得以非常快的速度设计，制造和测试定制机器人操纵器成为可能。该项目的目的之一是探索和理解如何使用这些新技术来设计一种完全不同类型的机器人机械手，这种机械手是为灵巧的机动而定制的。该项目的第二个目标是增加我们对灵巧本身的基本理解。最终目标是使机器人能够发挥其潜力。这样一来，机器人将开始在我们的日常生活中发挥越来越重要的作用，并将激励年轻学生为STEM职业做准备和追求。该项目的主要研究目标是形式化新的数学模型和计算方法，以协同设计灵巧机器人操作任务的机械结构和控制策略。通过这种方法，控制策略可以大大简化，关节停止和顺应性等机械特性可以被动地提高操作任务的鲁棒性，同时降低感知和驱动要求。捕捉在人类行为中观察到的特定操作能力家族的抓网，集中了本项目的努力，并指导了开发的设计过程。

Dexterous manipulation is a Grand Challenge in robotics today. Dexterity is required for robots working in the home, in space, for medical applications, disaster scenarios, and flexible manufacturing. Robots in these environments must be able to maneuver, lift, and handle objects, use them as tools, and competently transfer objects from one secure hold to another in a wide variety of uncertain situations. Such operations are very difficult for robots and present a barrier to their wider adoption. Recent advances in rapid prototyping and digital manufacturing have made it possible to design, manufacture, and test custom robot manipulators at a very rapid pace. One aim of this project is to explore and understand how these new technologies may be used to design a radically different type of robot manipulator that is customized for dexterous maneuvers. A second aim of the project is to increase our fundamental understanding of dexterity itself. The end goal is to enable robots to live up to their potential. In so doing, robots will begin to play an increasingly important role in our daily lives, and will inspire young students to prepare for, and pursue STEM careers.The key research goal of this project is to formalize novel mathematical models and computational approaches to co-design mechanical structures and control policies for dexterous robotic manipulation tasks. Through this approach, control policies can be made significantly simpler, and mechanical features such as joint stops and compliance can passively improve the robustness of the manipulation tasks while reducing sensing and actuation requirements. Grasp nets, which capture specific families of manipulation capabilities observed in human performances, focus the efforts of this project and guide the design processes that are developed.