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

RI：媒介：协作研究：机器人手：理解和实施自适应抓取

• 批准号: 0904504
• 负责人: Marco Santello
• 金额: $ 23.6万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0904504&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Robotic

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.

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。该项目正在定义低复杂度机器人手的基础，这些机器人手可以在嘈杂和非结构化的环境中抓取各种各样的物体。新一代的移动的和人形机器人仍然缺乏基本的？手？能够可靠地抓取物体。传统上，机器人手被构建为拟人的、高自由度（DOF）的机构，其昂贵且难以控制。该研究小组正在开发基于定义手部机制的技术，这些机制捕捉了人类抓握的两个关键特征，即手部姿势的多功能性和低维度。降低复杂性带来了巨大的好处。确定手部关节、传感器和致动器的最小数量可以降低成本并加速研究，因为低复杂度的手部可以很容易地制造，设计可以快速迭代，控制可以简化。这些想法是用来建立一个低成本，低自由度的抓取设备，是基于硬人类抓取数据。此外，新的手部设计正在模拟中进行测试，以便构建功能上经过验证的机器人抓取任务的硬件。重要的研究成果包括：开发一种新的低维，低成本的机器人手;实验，以获得人类抓取和自适应顺应性的见解;和抓取的机器学习算法。更广泛的影响包括：这些活动包括：神经科学和机器人技术之间的合作;手部研究人员的硬件设计方法和计算工具;在真实的环境中提供强大的抓取能力，如用于家庭护理和帮助老年人和残疾人的机器人;在神经控制和基于降维的假肢设备之间建立联系;以及传播建模和模拟抓取软件。