Abstract
In this study, deep learning based grasping using a robot has been discussed. A large amount of training data is required for good performance in deep learning. The training data is usually collected with a real robot. However, it is difficult to collect the data sufficient for training the network in terms of time and cost. Therefore, this study presents a method for collecting the training data based on a robot simulator as well as a real robot. The simulation system is composed of a robot, the work environment, and a 2-finger gripper. The convolutional neural network (CNN) was used for training where its input is the RGB image of the object and its output is the pose of the gripper. Furthermore, the ensemble learning method was used to combine real data and simulation data. It is shown that the ensemble learning method that combines multiple classifiers can lead to a higher grasping success rate than a single classifier.
| Original language | English |
|---|---|
| Title of host publication | ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 1030-1034 |
| Number of pages | 5 |
| Volume | 2017-October |
| ISBN (Electronic) | 9788993215137 |
| DOIs | |
| Publication status | Published - 2017 Dec 13 |
| Event | 17th International Conference on Control, Automation and Systems, ICCAS 2017 - Jeju, Korea, Republic of Duration: 2017 Oct 18 → 2017 Oct 21 |
Other
| Other | 17th International Conference on Control, Automation and Systems, ICCAS 2017 |
|---|---|
| Country | Korea, Republic of |
| City | Jeju |
| Period | 17/10/18 → 17/10/21 |
Fingerprint
Keywords
- Convolutional Neural Network
- Deep learning
- Ensemble learning
- Robot grasping
- Simulator robot
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
Learning to grasp objects based on ensemble learning combining simulation data and real data. / Na, Yong Ho; Jo, Hyunjun; Song, Jae-Bok.
ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings. Vol. 2017-October IEEE Computer Society, 2017. p. 1030-1034.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Learning to grasp objects based on ensemble learning combining simulation data and real data
AU - Na, Yong Ho
AU - Jo, Hyunjun
AU - Song, Jae-Bok
PY - 2017/12/13
Y1 - 2017/12/13
N2 - In this study, deep learning based grasping using a robot has been discussed. A large amount of training data is required for good performance in deep learning. The training data is usually collected with a real robot. However, it is difficult to collect the data sufficient for training the network in terms of time and cost. Therefore, this study presents a method for collecting the training data based on a robot simulator as well as a real robot. The simulation system is composed of a robot, the work environment, and a 2-finger gripper. The convolutional neural network (CNN) was used for training where its input is the RGB image of the object and its output is the pose of the gripper. Furthermore, the ensemble learning method was used to combine real data and simulation data. It is shown that the ensemble learning method that combines multiple classifiers can lead to a higher grasping success rate than a single classifier.
AB - In this study, deep learning based grasping using a robot has been discussed. A large amount of training data is required for good performance in deep learning. The training data is usually collected with a real robot. However, it is difficult to collect the data sufficient for training the network in terms of time and cost. Therefore, this study presents a method for collecting the training data based on a robot simulator as well as a real robot. The simulation system is composed of a robot, the work environment, and a 2-finger gripper. The convolutional neural network (CNN) was used for training where its input is the RGB image of the object and its output is the pose of the gripper. Furthermore, the ensemble learning method was used to combine real data and simulation data. It is shown that the ensemble learning method that combines multiple classifiers can lead to a higher grasping success rate than a single classifier.
KW - Convolutional Neural Network
KW - Deep learning
KW - Ensemble learning
KW - Robot grasping
KW - Simulator robot
UR - http://www.scopus.com/inward/record.url?scp=85044455650&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044455650&partnerID=8YFLogxK
U2 - 10.23919/ICCAS.2017.8204368
DO - 10.23919/ICCAS.2017.8204368
M3 - Conference contribution
AN - SCOPUS:85044455650
VL - 2017-October
SP - 1030
EP - 1034
BT - ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings
PB - IEEE Computer Society
ER -