Learning Robot Trajectories subject to Kinematic Joint Constraints
Jonas C. Kiemel,Torsten Kröger,Jonas C. Kiemel,Torsten Kröger
We present an approach to learn fast and dynamic robot motions without exceeding limits on the position θ, velocity $\dot \theta $ , acceleration $\ddot \theta $ and jerk $\dddot \theta $ of each robot joint. Movements are generated by mapping the predictions of a neural network to safely executable joint accelerations. The neural network is invoked periodically and trained via reinforcement learn...
