RI: Medium: Collaborative Research: Robotic Hands: Understanding and Implementing Adaptive Grasping

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。该项目为低复杂度的机器人手奠定了基础，使其能够在嘈杂和非结构化的环境中抓取各种各样的物体。新一代的移动机器人和人形机器人仍然缺乏基本的手。能够可靠地抓住物体。机械手传统上是拟人化的、高自由度（DOF）机构，价格昂贵且难以控制。研究小组正在开发基于定义手部机制的技术，该机制捕捉了人类抓取的两个关键特征，即手部姿势的多功能性和低维性。减少复杂性带来了主要的好处。确定手关节、传感器和执行器的最小数量可以降低成本，加快研究速度，因为低复杂度的手可以很容易地制造，设计可以快速迭代，控制可以简化。这些想法被用来建立一个低成本，低自由度的抓取装置，是基于硬的人类抓取数据。此外，新的手设计正在进行模拟测试，以便构建硬件，在功能上证明机器人抓取任务。重要研究成果包括：研制新型低维、低成本机械手；从人类抓取和适应性依从中获得见解的实验；以及抓取的机器学习算法。更广泛的影响包括：神经科学和机器人之间的合作；手部研究者的硬件设计方法与计算工具在真实环境中提供强大的抓取能力，例如用于家庭护理和帮助老年人和残疾人的机器人；基于降维建立神经控制与假肢装置之间的联系；并传播建模与仿真抓取软件。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). This project is defining the basis for lower-complexity robotic hands that can grasp a wide variety of objects in noisy and unstructured environments. The new generation of mobile and humanoid robots still lacks basic ?hands? that can reliably grasp objects. Robot hands have been traditionally built as anthropomorphic, high degree-of-freedom (DOF) mechanisms that are expensive and difficult to control. The research team is developing technologies based on defining hand mechanisms that capture two key features of human grasping, versatility and low dimensionality of hand postures. Reducing complexity brings major benefits. Determining the minimal number of hand joints, sensors and actuators can reduce costs and speed research as low-complexity hands can be easily fabricated, designs can be quickly iterated, and control can be simplified. These ideas are used to build a low-cost, low DOF grasping device that is based on hard human grasping data. Further, the new hand designs are being tested in simulation so as to build hardware that is functionally proven for robotic grasping tasks. Important research outcomes include: development of a new low-dimensional, low-cost robotic hand; experiments to gain insights from human grasping and adaptive compliance; and machine learning algorithms for grasping. Broader impacts include: collaboration between neuroscience and robotics; hardware design methods and computational tools for hand researchers; providing robust grasping capabilities in real environments such as robots for home care and assistance for the elderly and disabled; establishing links between neural control and prosthetic devices based on dimensionality reduction; and dissemination of modeling and simulation grasping software.