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.

Learning PhysicsBased Manipulation in Clutter: Combining ImageBased Generalization and LookAhead Planning Physicsbased manipulation in clutter involves complex interaction between multiple objects. In this paper, we consider the problem of learning, from interaction in a physics simulator, manipulation skills to solve this multistep sequential decision making problem in the real world. Our approach has two key properties: (i) the ability to generalize and transfer manipulation skills (over the type, shape, and number of objects in the scene) using an abstract imagebased representation that enables a neural network to learn useful features; and (ii) the ability to perform lookahead planning in the image space using a physics simulator, which is essential for such multistep problems. We show, in sets of simulated and realworld experiments (video available on https:youtu.beEmkUQfyvwkY), that by learning to evaluate actions in an abstract imagebased representation of the real world, the robot can generalize and adapt to the object shapes in challenging realworld environments.