2021
DOI: 10.48550/arxiv.2101.01052
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Imitation Learning for High Precision Peg-in-Hole Tasks

Sagar Gubbi,
Shishir Kolathaya,
Bharadwaj Amrutur

Abstract: Industrial robot manipulators are not able to match the precision and speed with which humans are able to execute contact rich tasks even to this day. Therefore, as a means overcome this gap, we demonstrate generative methods for imitating a peg-in-hole insertion task in a 6-DOF robot manipulator. In particular, generative adversarial imitation learning (GAIL) is used to successfully achieve this task with a 6 µm peg-hole clearance on the Yaskawa GP8 industrial robot. Experimental results show that the policy … Show more

Help me understand this report
View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 14 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?