23 citations · 33 across the 9 of their papers we have counts for
9 papers
Safe Self-Supervised Learning in Real of Visuo-Tactile Feedback Policies for Industrial Insertion
Letian Fu, Huang Huang, Lars Berscheid +3
Industrial insertion tasks are often performed repetitively with parts that are subject to tight tolerances and prone to breakage. Learning an industrial insertion policy in real i…
SpeedFolding: Learning Efficient Bimanual Folding of Garments
Yahav Avigal, Lars Berscheid, Tamim Asfour +2
Folding garments reliably and efficiently is a long standing challenge in robotic manipulation due to the complex dynamics and high dimensional configuration space of garments. An…
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…
Jerk-limited Real-time Trajectory Generation with Arbitrary Target States
Lars Berscheid, Torsten Kröger
We present Ruckig, an algorithm for Online Trajectory Generation (OTG) respecting third-order constraints and complete kinematic target states. Given any initial state of a system…
Robot Learning of 6 DoF Grasping using Model-based Adaptive Primitives
Lars Berscheid, Christian Friedrich, Torsten Kröger
Robot learning is often simplified to planar manipulation due to its data consumption. Then, a common approach is to use a fully-convolutional neural network to estimate the reward…
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…