TY - GEN
T1 - Robot Learning of Shifting Objects for Grasping in Cluttered Environments
AU - Berscheid, Lars
AU - Meissner, Pascal
AU - Kroger, Torsten
PY - 2019/11
Y1 - 2019/11
N2 - Robotic grasping in cluttered environments is often infeasible due to obstacles preventing possible grasps. Then, pre-grasping 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 non-prehensible 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 data-efficient learning. Third, we apply a real-world solution to the industrial task of bin picking, resulting in the ability to empty bins completely. The system is trained in a self-supervised manner with around 25 000 grasp and 2500 shift actions. Our robot is able to grasp and file objects with 274±3 picks per hour. Furthermore, we demonstrate the system's ability to generalize to novel objects.
AB - Robotic grasping in cluttered environments is often infeasible due to obstacles preventing possible grasps. Then, pre-grasping 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 non-prehensible 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 data-efficient learning. Third, we apply a real-world solution to the industrial task of bin picking, resulting in the ability to empty bins completely. The system is trained in a self-supervised manner with around 25 000 grasp and 2500 shift actions. Our robot is able to grasp and file objects with 274±3 picks per hour. Furthermore, we demonstrate the system's ability to generalize to novel objects.
UR - http://www.scopus.com/inward/record.url?scp=85081155762&partnerID=8YFLogxK
U2 - 10.1109/IROS40897.2019.8968042
DO - 10.1109/IROS40897.2019.8968042
M3 - Published conference contribution
AN - SCOPUS:85081155762
SN - 978-1-7281-4005-6
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 612
EP - 618
BT - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Y2 - 3 November 2019 through 8 November 2019
ER -