  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.
