论文标题
敞开的手臂:开源手臂,手和控制
Open Arms: Open-Source Arms, Hands & Control
论文作者
论文摘要
Open Arms是一个新型的开源平台,该平台具有现实的人类机器人手和手臂硬件,并具有28个自由度(DOF),旨在扩展人形机器人握把和操纵的能力和可访问性。敞开的武器框架包括开放的SDK和开发环境,模拟工具以及用于建造和操作敞开的应用程序开发工具。本文描述了这些手控制,感应,机制,美学设计以及制造业及其实际应用,并使用远程手工护理机器人进行了现实应用。从2015年到2022年,作者设计并确定了开放武器的制造,作为低成本,高功能的机器人武器硬件和软件框架,以服务类人体机器人的应用以及对低成本假肢的紧急需求,作为Hanson Robots Sophia Robot Sophia Robot Platform的一部分。使用消费产品制造的技术,我们着手定义模块化的低成本技术,以近似人类手的敏捷性和敏感性。为了证明我们的手的敏捷性和控制,我们提出了一个生成的残留CNN(GGR-CNN)模型,该模型可以以实时速度(22ms)的各种对象的输入图像产生强大的抗抑制剂。我们使用标准的康奈尔(Cornell)握把数据集上的模型体系结构实现了92.4%的最新精度,该数据集包含各种各样的家庭对象。
Open Arms is a novel open-source platform of realistic human-like robotic hands and arms hardware with 28 Degree-of-Freedom (DoF), designed to extend the capabilities and accessibility of humanoid robotic grasping and manipulation. The Open Arms framework includes an open SDK and development environment, simulation tools, and application development tools to build and operate Open Arms. This paper describes these hands controls, sensing, mechanisms, aesthetic design, and manufacturing and their real-world applications with a teleoperated nursing robot. From 2015 to 2022, the authors have designed and established the manufacturing of Open Arms as a low-cost, high functionality robotic arms hardware and software framework to serve both humanoid robot applications and the urgent demand for low-cost prosthetics, as part of the Hanson Robotics Sophia Robot platform. Using the techniques of consumer product manufacturing, we set out to define modular, low-cost techniques for approximating the dexterity and sensitivity of human hands. To demonstrate the dexterity and control of our hands, we present a Generative Grasping Residual CNN (GGR-CNN) model that can generate robust antipodal grasps from input images of various objects in real-time speeds (22ms). We achieved state-of-the-art accuracy of 92.4% using our model architecture on a standard Cornell Grasping Dataset, which contains a diverse set of household objects.