Adjusting Weight of Action Decision in Exploration for Logistics Warehouse Picking Learning

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.