3 citations · 3 across the 3 of their papers we have counts for
5 papers · 1 filter
Learning a Generative Transition Model for Uncertainty-Aware Robotic Manipulation
Lars Berscheid, Pascal Meißner, Torsten Kröger
Robot learning of real-world manipulation tasks remains challenging and time consuming, even though actions are often simplified by single-step manipulation primitives. In order to…
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…
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…
TrueÆdapt: Learning Smooth Online Trajectory Adaptation with Bounded Jerk, Acceleration and Velocity in Joint Space
Jonas C. Kiemel, Robin Weitemeyer, Pascal Meißner +1
We present TrueÆdapt, a model-free method to learn online adaptations of robot trajectories based on their effects on the environment. Given sensory feedback and future waypoints o…
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…