3 papers
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
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.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…