  We present True{\AE}dapt, a model-free method to learn online adaptations of
robot trajectories based on their effects on the environment. Given sensory
feedback and future waypoints of the original trajectory, a neural network is
trained to predict joint accelerations at regular intervals. The adapted
trajectory is generated by linear interpolation of the predicted accelerations,
leading to continuously differentiable joint velocities and positions. Bounded
jerks, accelerations and velocities are guaranteed by calculating the range of
valid accelerations at each decision step and clipping the network's output
accordingly. A deviation penalty during the training process causes the adapted
trajectory to follow the original one. Smooth movements are encouraged by
penalizing high accelerations and jerks. We evaluate our approach by training a
simulated KUKA iiwa robot to balance a ball on a plate while moving and
demonstrate that the balancing policy can be directly transferred to a real
robot.
