Realtime Grasp Pose Estimation for Novel Objects in Densely Cluttered Environment. Grasping of novel objects in pick and place applications is a fundamental and challenging problem in robotics, specifically for complexshaped objects. It is observed that the wellknown strategies like textiti) grasping from the centroid of object and textitii) grasping along the major axis of the object often fails for complexshaped objects. In this paper, a realtime grasp pose estimation strategy for novel objects in robotic pick and place applications is proposed. The proposed technique estimates the object contour in the point cloud and predicts the grasp pose along with the object skeleton in the image plane. The technique is tested for the objects like ball container, hand weight, tennis ball and even for complex shape objects like blower (nonconvex shape). It is observed that the proposed strategy performs very well for complex shaped objects and predicts the valid grasp configurations in comparison with the above strategies. The experimental validation of the proposed grasping technique is tested in two scenarios, when the objects are placed distinctly and when the objects are placed in dense clutter. A grasp accuracy of 88.16% and 77.03% respectively are reported. All the experiments are performed with a real UR10 robot manipulator along with WSG50 twofinger gripper for grasping of 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.