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Learning to Grasp Unknown Objects using Weighted Random Forest Algorithm from Selective Image and Point Cloud Feature

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Date Issued:
2014
Abstract/Description:
This method demonstrates an approach to determine the best grasping location on an unknown object using Weighted Random Forest Algorithm. It used RGB-D value of an object as input to find a suitable rectangular grasping region as the output. To accomplish this task, it uses a subspace of most important features from a very high dimensional extensive feature space that contains both image and point cloud features. Usage of most important features in the grasping algorithm has enabled the system to be computationally very fast while preserving maximum information gain. In this approach, the Random Forest operates using optimum parameters e.g. Number of Trees, Number of Features at each node, Information Gain Criteria etc. ensures optimization in learning, with highest possible accuracy in minimum time in an advanced practical setting. The Weighted Random Forest chosen over Support Vector Machine (SVM), Decision Tree and Adaboost for implementation of the grasping system outperforms the stated machine learning algorithms both in training and testing accuracy and other performance estimates. The Grasping System utilizing learning from a score function detects the rectangular grasping region after selecting the top rectangle that has the largest score. The system is implemented and tested in a Baxter Research Robot with Parallel Plate Gripper in action.
Title: Learning to Grasp Unknown Objects using Weighted Random Forest Algorithm from Selective Image and Point Cloud Feature.
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Name(s): Iqbal, Md Shahriar, Author
Behal, Aman, Committee Chair
Boloni, Ladislau, Committee Member
Haralambous, Michael, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2014
Publisher: University of Central Florida
Language(s): English
Abstract/Description: This method demonstrates an approach to determine the best grasping location on an unknown object using Weighted Random Forest Algorithm. It used RGB-D value of an object as input to find a suitable rectangular grasping region as the output. To accomplish this task, it uses a subspace of most important features from a very high dimensional extensive feature space that contains both image and point cloud features. Usage of most important features in the grasping algorithm has enabled the system to be computationally very fast while preserving maximum information gain. In this approach, the Random Forest operates using optimum parameters e.g. Number of Trees, Number of Features at each node, Information Gain Criteria etc. ensures optimization in learning, with highest possible accuracy in minimum time in an advanced practical setting. The Weighted Random Forest chosen over Support Vector Machine (SVM), Decision Tree and Adaboost for implementation of the grasping system outperforms the stated machine learning algorithms both in training and testing accuracy and other performance estimates. The Grasping System utilizing learning from a score function detects the rectangular grasping region after selecting the top rectangle that has the largest score. The system is implemented and tested in a Baxter Research Robot with Parallel Plate Gripper in action.
Identifier: CFE0005509 (IID), ucf:50358 (fedora)
Note(s): 2014-12-01
M.S.E.E.
Engineering and Computer Science, Electrical Engr and Comp Sci
Masters
This record was generated from author submitted information.
Subject(s): Robotic Grasping -- Weighted Random Forest -- Support Vector Machine -- Decision Tree -- Adaboost -- Image and Point Cloud Feature -- Baxter Research Robot
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0005509
Restrictions on Access: public 2014-12-15
Host Institution: UCF

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