Object Grasping for Robot Manipulator Based on Behavioral Cloning
| Autor: | You-Jun Huang, 黃宥竣 |
|---|---|
| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 A method based on behavioral cloning for a robot manipulator to grasp objects is implemented and a dual vision neural network model is proposed to enable the deep neural network (DNN) to effectively learn the task-related features so that the robot manipulator can perform the desired behavior of grasping object. There are three main parts: (1) imitation learning, (2) deep neural network, and (3) training sample collection. In the imitation learning, the behavioral cloning method combined with the dataset aggregation algorithm is used to let the DNN learn the behaviors demonstrated by the demonstration data and to reduce the compounding errors of the trained neural network. In the deep neural network, a dual vision neural network model based on the convolutional neural network is proposed to optimize the network model to learn the recognition, location, and task-related features of the target object. The inputs of the dual vision network model are the RGB images of both the external camera and eye-in-hand camera, and the outputs of manipulator. First, the images of the two cameras are respectively input to the corresponding convolution layer. The outputs of the two convolution outputs and the outputs of robot manipulator are respectively joined by a fully connected layer, and the two joint results are processed by multiple fully connected layers. Finally, the outputs of network model are commands to control the robot manipulator and gripper. In the training sample collection, the domain randomization and data set aggregation algorithms are used to generate various training samples, which make the DNN more robust. In the experimental results, the success rate of the execution tasks of the three network models (the reference network model, the dual visual network model V1, and the dual visual network model V2) is compared. The experimental results illustrate that the proposed method can indeed improve the learning effect of DNN. Moreover, when the grapping task fails, the DNN will perform the gripping behavior again to let the robot manipulator be able to perform the desired behavior. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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