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.RO2020
TrueRMA: Learning Fast and Smooth Robot Trajectories with Recursive Midpoint Adaptations in Cartesian Space
Jonas C. Kiemel, Pascal Meißner, Torsten Kröger
We present TrueRMA, a data-efficient, model-free method to learn cost-optimized robot trajectories over a wide range of starting points and endpoints. The key idea is to calculate…
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…