3 citations · 4 across the 9 of their papers we have counts for
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cs.RO2020
Learning Robot Trajectories subject to Kinematic Joint Constraints
Jonas C. Kiemel, Torsten Kröger
We present an approach to learn fast and dynamic robot motions without exceeding limits on the position , velocity , acceleration and jerk of each robo…
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
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…