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.

Robot Learning of Shifting Objects for Grasping in Cluttered Environments Robotic grasping in cluttered environments is often infeasible due to obstacles preventing possible grasps. Then, pregrasping manipulation like shifting or pushing an object becomes necessary. We developed an algorithm that can learn, in addition to grasping, to shift objects in such a way that their grasp probability increases. Our research contribution is threefold: First, we present an algorithm for learning the optimal pose of manipulation primitives like clamping or shifting. Second, we learn nonprehensible actions that explicitly increase the grasping probability. Making one skill (shifting) directly dependent on another (grasping) removes the need of sparse rewards, leading to more dataefficient learning. Third, we apply a realworld solution to the industrial task of bin picking, resulting in the ability to empty bins completely. The system is trained in a selfsupervised manner with around 25 000 grasp and 2500 shift actions. Our robot is able to grasp and file objects with 274xc2xb13 picks per hour. Furthermore, we demonstrate the systemxe2x80x99s ability to generalize to novel objects.