TY - GEN
T1 - Deep learning-based object understanding for robotic manipulation
AU - Moon, Jong Sul
AU - Jo, Hyunjun
AU - Song, Jae Bok
N1 - Funding Information:
This research was supported by the MOTIE under the Industrial Foundation Technology Development Program supervised by the KEIT (No. 20008613)
Publisher Copyright:
© 2020 Institute of Control, Robotics, and Systems - ICROS.
PY - 2020/10/13
Y1 - 2020/10/13
N2 - Manipulation of objects by a robot arm requires an understanding of the various properties of the object. The robot needs a lot of information for object manipulation, there are few algorithms to estimate such information simultaneously. In this study, we propose an object understanding network (OUNet) based on deep learning that simultaneously estimates three key properties for robot object manipulation: object state, contact position for object manipulation, and manipulation type. The object state means whether an openable object is open or closed. The contact position and manipulation type for manipulating objects means where and what the robot should do to change the object state. Usingthis information, it is expected that the robot will be able to select the appropriate manipulation for the current situation of the given object. Experiments were conducted to verify the performance of the OUNet, and it was shown that three key properties can be successfully detected.
AB - Manipulation of objects by a robot arm requires an understanding of the various properties of the object. The robot needs a lot of information for object manipulation, there are few algorithms to estimate such information simultaneously. In this study, we propose an object understanding network (OUNet) based on deep learning that simultaneously estimates three key properties for robot object manipulation: object state, contact position for object manipulation, and manipulation type. The object state means whether an openable object is open or closed. The contact position and manipulation type for manipulating objects means where and what the robot should do to change the object state. Usingthis information, it is expected that the robot will be able to select the appropriate manipulation for the current situation of the given object. Experiments were conducted to verify the performance of the OUNet, and it was shown that three key properties can be successfully detected.
KW - Clustering
KW - Deep learning
KW - Manipulation
KW - Object understanding
UR - http://www.scopus.com/inward/record.url?scp=85098095008&partnerID=8YFLogxK
U2 - 10.23919/ICCAS50221.2020.9268261
DO - 10.23919/ICCAS50221.2020.9268261
M3 - Conference contribution
AN - SCOPUS:85098095008
T3 - International Conference on Control, Automation and Systems
SP - 1
EP - 5
BT - 2020 20th International Conference on Control, Automation and Systems, ICCAS 2020
PB - IEEE Computer Society
T2 - 20th International Conference on Control, Automation and Systems, ICCAS 2020
Y2 - 13 October 2020 through 16 October 2020
ER -