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

True{\AE}dapt: Learning Smooth Online Trajectory Adaptation with Bounded Jerk, Acceleration and Velocity in Joint Space