A method for end-to-end robotic arm control based on deep learning

A technology of deep learning and manipulators, applied in manipulators, program-controlled manipulators, manufacturing tools, etc., can solve the problems of high data cost and difficult data acquisition

Active Publication Date: 2019-06-18
ZHEJIANG UNIV
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the field of control, sometimes data is not easy to obtain or the cost of obtaining data is high
For example, the motion control data of the robotic arm mentioned in this article limits the application of deep learning.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A method for end-to-end robotic arm control based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0022] The invention discloses an end-to-end robotic arm control method based on deep learning,

[0023] Step 1, collecting image information and control instructions and state information of the robotic arm at a certain frequency when the robotic arm is in motion;

[0024] Step 2, performing network processing on the image information, control instructions and status information collected in step 1:

[0025] The network includes an image processing network and a control strategy network. The image processing network is used to reduce the dimensionality of the original image, so that the image input and the state input dimension of the manipulator are on the same order of magnitude;

[0026] The control strategy network outputs the control instructions executed on the robotic arm, initializes the weight of the image processing network, the image information is used as input, and the status information of the robotic arm is used as output, training and initializing the image pr...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a method for conducting arm control through deep learning. The method comprises the steps that firstly, an image of the motion process of a mechanical arm is collected, meanwhile, a control command of the arm is recorded at a certain frequency, and a controller expressed through a deep neural network is obtained through an end-to-end training method. On that basis, close observation shows that the control structure is expressed through the deep neural network, the motion error of the arm can be further reduced through the end-to-end training method, and obstacle avoidance motion can still be achieved greatly under the condition that an obstacle exists. The method is flexible to implement, samples needed by training are decreased to a great extent, and the method has a great advantage for the condition that large samples cannot be obtained easily for motion of the mechanical arm.

Description

technical field [0001] The invention belongs to the field of deep reinforcement learning, and in particular relates to an end-to-end motion control method, which greatly reduces the sample data required for training. Background technique [0002] In recent years, deep learning has developed rapidly in academia, especially in pattern recognition. In many traditional recognition tasks, the recognition rate has been significantly improved. Many other fields also try to use deep learning to solve some problems in this field. [0003] There have been some studies on the application of deep learning applications in the field of control, especially the combination with reinforcement learning, showing its unique advantages. Deep reinforcement learning is a field that combines deep learning and reinforcement learning. It can realize a new algorithm for end-to-end learning from perception to action. Simply put, it is the same as humans, input perceptual information such as vision, ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): B25J9/16B25J13/08B25J9/22
CPCB25J9/1602B25J9/1666B25J13/08
Inventor 刘勇王志磊
Owner ZHEJIANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap