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)提供资金的。该项目正在为低复杂性的机械手奠定基础，这种机械手可以在嘈杂和非结构化的环境中抓取各种物体。新一代移动和类人机器人仍然缺乏基本的手？能够可靠地抓住物体。传统上，机械手被构建为拟人化的高自由度(DOF)机构，这些机构昂贵且难以控制。研究团队正在开发基于定义手机制的技术，这些机制捕捉到人类抓取的两个关键特征，即手势的多功能性和低维。降低复杂性带来了重大好处。确定手关节、传感器和执行器的最小数量可以降低成本和加快研究速度，因为低复杂性的手可以很容易地制造，设计可以快速迭代，控制可以简化。这些想法被用来建造一种低成本、低自由度的抓取装置，它基于人类的硬抓取数据。此外，新的手设计正在进行模拟测试，以建立在功能上被证明适用于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、低成本的机器手；从人类抓取和适应性顺应性中获得洞察力的实验；以及用于抓取的机器学习算法。更广泛的影响包括：神经科学和机器人学之间的合作；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；在降维的基础上建立神经控制和假肢设备之间的联系；以及传播建模和模拟抓取软件。

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.