Paper Info
Title
Imitation Learning System Design With Small Training Data For Flexible Tool Manipulation
Abstract
Population aging has become a major problem in developed countries. As the labor force declines, robot arms are expected to replace human labor for simple tasks. A robotic arm attaches a tool specialized for a task and acquires the movement through teaching by an engineer with expert knowledge. However, the number of such engineers is limited; therefore, a teaching method that can be used by non-technical personnel is necessitated. As a teaching method, deep learning can be used to imitate human behavior and tool usage. However, deep learning requires a large amount of training data for learning. In this study, the target task of the robot is to sweep multiple pieces of dirt using a broom. The proposed learning system can estimate the initial parameters for deep learning based on experience, as well as the shape and physical properties of the tools. It can reduce the number of training data points when learning a new tool. A virtual reality system is used to move the robot arm easily and safely, as well as to create training data for imitation. In this study, cleaning experiments are conducted to evaluate the effectiveness of the proposed method. The experimental results confirm that the proposed method can accelerate the learning speed of deep learning and acquire cleaning ability using a small amount of training data.
Year
DOI
Venue
2021
10.20965/ijat.2021.p0669
INTERNATIONAL JOURNAL OF AUTOMATION TECHNOLOGY
Keywords
DocType
Volume
system integration, tool manipulation, imitation learning, deep learning, human support robot
Journal
15
Issue
ISSN
Citations 
5
1881-7629
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Harumo Sasatake100.34
Ryosuke Tasaki214.07
Takahito Yamashita300.34
Naoki Uchiyama44822.80