Object Rearrangement with Nested Nonprehensile Manipulation Actions This paper considers the problem of rearrangement planning, i.e finding a sequence of manipulation actions that displace multiple objects from an initial configuration to a given goal configuration. Rearrangement is a critical skill for robots so that they can effectively operate in confined spaces that contain clutter. Examples of tasks that require rearrangement include packing objects inside a bin, wherein objects need to lay according to a predefined pattern. In tight bins, collisionfree grasps are often unavailable. Nonprehensile actions, such as pushing and sliding, are preferred because they can be performed using minimalistic endeffectors that can easily be inserted in the bin. Rearrangement with nonprehensile actions is a challenging problem as it requires reasoning about object interactions in a combinatorially large configuration space of multiple objects. This work revisits several existing rearrangement planning techniques and introduces a new one that exploits nested nonprehensile actions by pushing several similar objects simultaneously along the same path, which removes the need to rearrange each object individually. Experiments in simulation and using a real Kuka robotic arm show the ability of the proposed approach to solve difficult rearrangement tasks while reducing the length of the endeffectorxe2x80x99s trajectories.

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.