Adjusting Weight of Action Decision in Exploration for Logistics Warehouse Picking Learning
Kato Yusuke, Nakamura Tomoaki, Nagai Takayuki, Yamanobe Natsuki, Nagata Kazuyuki, Ozawa Jun
Abstract
The purpose of this study is for a robot to learn picking motions in a logistics warehouse environment. The picking operation performed by a robot often fails owing to the inclination of items placed on a shelf, as well as the minimum clearance between the products and their vinyl packaging. Therefore, we considered acquiring a specific motion trajectory by reinforcement learning. However, because numerous types of items are handled in logistics warehouses, efficient learning is required. Therefore, in this research, we propose a method to efficiently exploration for learning picking an object by determining a focus exploration area for learning based on previous results of different objects.
BibTeX
@inproceedings{iros2019_adjustingweighto,
title = {Adjusting Weight of Action Decision in Exploration for Logistics Warehouse Picking Learning},
author = {Kato Yusuke and Nakamura Tomoaki and Nagai Takayuki and Yamanobe Natsuki and Nagata Kazuyuki and Ozawa Jun},
booktitle = {IROS 2019},
year = {2019}
}