3 papers
cs.RO2020
Self-supervised Learning for Precise Pick-and-place without Object Model
Lars Berscheid, Pascal Meißner, Torsten Kröger
Flexible pick-and-place is a fundamental yet challenging task within robotics, in particular due to the need of an object model for a simple target pose definition. In this work, t…
cs.RO2019
Robot Learning of Shifting Objects for Grasping in Cluttered Environments
Lars Berscheid, Pascal Meißner, Torsten Kröger
Robotic grasping in cluttered environments is often infeasible due to obstacles preventing possible grasps. Then, pre-grasping manipulation like shifting or pushing an object becom…
cs.RO2019
Improving Data Efficiency of Self-supervised Learning for Robotic Grasping
Lars Berscheid, Thomas Rühr, Torsten Kröger
Given the task of learning robotic grasping solely based on a depth camera input and gripper force feedback, we derive a learning algorithm from an applied point of view to signifi…