基於行為複製之機械手臂的物件夾取

Object Grasping for Robot Manipulator Based on Behavioral Cloning

黃宥竣(You-Jun Huang)

指導教授 ： 翁慶昌

共同指導教授 ： 蔡奇謚(Chi-Yi Tsai)

淡江大學/工學院/電機工程學系機器人工程碩士班/碩士(2018年)

https://doi.org/10.6846/TKU.2018.00855

摘要

本論文實現一個基於行為複製之機械手臂夾取物件的方法，並提出一個雙視覺網路模型來讓深度神經網路能夠有效地學習任務相關的特徵，使機械手臂可以執行期望之機械手臂夾取物件的行為。主要有三個部分：(1)模仿學習、(2)深度神經網路、及(3)訓練樣本蒐集。在模仿學習的部分，本論文採用行為複製方法並結合資料集聚合演算法來讓深度神經網路學習範例資料所示範的行為，並且降低訓練過的神經網路之複合誤差。在深度神經網路的部分，本論文提出一個基於卷積神經網路的雙視覺網路模型來優化網路模型對目標物件的辨識、定位、及任務相關的特徵之學習。雙視覺網路模型之輸入為外部攝影機與機械手臂手部攝影機的RGB影像以及機械手臂之回授輸出。首先將兩個攝影機的RGB影像分別輸入到對應的卷積層，然後各用一個全連接層分別對兩個卷積之輸出以及機械手臂之回授輸出做接合，再用多個全連接層來處理這兩個接合結果，最後網路模型之輸出為控制機械手臂及夾具的命令。在訓練樣本蒐集的部分，本論文利用領域隨機化及資料集聚合演算法來產生各種的訓練樣本，使深度神經網路更具有強健性。在實驗結果的部分，本論文比較三種網路模型之執行物件夾取任務的成功率，分別為基準網路模型、雙視覺網路模型V1、及雙視覺網路模型V2。從實驗結果可知，本論文所提出之雙視覺網路模型V2的方法確實可以提高深度神經網路之學習效果。此外，在夾取任務失敗時，深度神經網路會再一次執行夾取行為來讓機械手臂能夠完成期望的行為。

關鍵字

機械手臂 ； 物件夾取 ； 模仿學習 ； 行為複製 ； 深度神經網路

並列摘要

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.

並列關鍵字

Robot Manipulator ； Object Grasping ； Imitation Learning ； Behavioral Cloning ； Deep Neural Network

參考文獻

[1] J.F. Engelberger, Robotics in Service, MIT Press, 1989.

[2] KUKA Manipulator, URL: http://www.kuka.com

[3] KUKA Specialized System for Riveting, URL: https://roboticsandautomationnews.com/2018/04/26/kuka-launches-specialised-system-for-riveting/16995/

[4] KUKA Manipulator Writing, URL: https://rushtips.com/kuka-robot-design-award

[5] A. Krizhevsky, I. Sutskever, and G.E. Hinton, “Imagenet Classification with Deep Convolutional Neural Networks,” in Proc. Advances Neural Inf. Process. Syst., pp.1106-1114, 2012.