Download or read book Probabilistic Learning of Robotic Grasping Strategy Based on Natural Language Object Descriptions written by Bharath Rao and published by . This book was released on 2018 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: Humans learn to be dexterous by interacting with a wide variety of objects in different contexts. Given the description of an object's physical attributes, humans can determine a proper strategy and grasp an object. This paper proposes an approach to determine grasping strategy for a 10 degree-of-freedom anthropomorphic robotic hand simply based on natural-language descriptions of an object. A probabilistic learning-based approach is proposed to help a robotic hand learn suitable grasp poses starting from the natural language description of the object. The solution involves a three-step learning model. In the first step, the information parsed from an object's natural-language descriptions are used to identify/recognize the object by making use of a novel nearestneighbor distance metric. In the second step, the probability distribution of grasp types for the given object is learned using a deep neural net which takes in object features as input. The labels for this grasp learning model is supplied from human grasping trials. The discrete, two-dimensional grasp type/size vector is mapped back to the ten-dimensional robot joint-angles configuration space using linear inverse-kinematics models. The grasping strategy generated by the proposed approach is evaluated both by simulation study and execution of the grasps on an AR10 robotic hand. Index Terms--robotic grasping, human grasp primitives, natural language processing, object features extraction, neural networks classification.