Probabilistic Learning of Robotic Grasping Strategy Based on Natural Language Object Descriptions

Released on by Bharath Rao
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  • Probabilistic Learning of Robotic Grasping Strategy Based on Natural Language Object Descriptions Book Detail

  • Author : Bharath Rao
  • Release Date : 2018
  • Publisher :
  • Genre : Electronic dissertations
  • Pages : 65
  • ISBN 13 :
  • File Size : 36,36 MB
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Probabilistic Learning of Robotic Grasping Strategy Based on Natural Language Object Descriptions by Bharath Rao PDF Summary

Book Description: 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.

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