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Probabilistic learning of robotic grasping strategy based on natural language object descriptions
Bharath Rao, Achyutha
Bharath Rao, Achyutha
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2018-07
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Electronic dissertation
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Abstract
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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Thesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Industrial Systems, and Manufacturing Engineering
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Wichita State University
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Copyright 2018 by Achyutha Bharath Rao
All Rights Reserved