论文标题
知识增强的灵巧抓握，不完整
Knowledge-Augmented Dexterous Grasping with Incomplete Sensing
论文作者
论文摘要
人类可以根据测量的物理属性或对象的先验知识来确定适当的策略来掌握对象。本文提出了一种方法，即通过使用标签或对象的描述使用拟人化机器人手来确定灵巧抓握的策略。对象属性是根据自然语言描述解析的，并用零售商网站刮擦的对象知识库进行增强。一个名为联合概率距离的新型度量定义是为了测量对象属性之间的距离。使用对象特征作为输入的深神经网络学习了给定对象的GRASP类型的概率分布。具有冗余自由度（DOF）的多指手手的动作由GRASP拓扑和尺度的线性逆界数模型控制。提出的方法产生的抓地力策略通过用AR10机器人手在锯耶机器人上的模拟和执行来评估。
Humans can determine a proper strategy to grasp an object according to the measured physical attributes or the prior knowledge of the object. This paper proposes an approach to determining the strategy of dexterous grasping by using an anthropomorphic robotic hand simply based on a label or a description of an object. Object attributes are parsed from natural-language descriptions and augmented with an object knowledge base that is scraped from retailer websites. A novel metric named joint probability distance is defined to measure distance between object attributes. The probability distribution of grasp types for the given object is learned using a deep neural network which takes in object features as input. The action of the multi-fingered hand with redundant degrees of freedom (DoF) is controlled by a linear inverse-kinematics model of grasp topology and scales. The grasping strategy generated by the proposed approach is evaluated both by simulation and execution on a Sawyer robot with an AR10 robotic hand.
