In this paper, we present, for the first time, a soft robot control system (SofToss) capable of throwing life-size objects toward target positions. SofToss is an open-loop controller based on deep reinforcement learning that generates, given the target position, an actuation pattern for the tossing task. To deal with the high non-linearity of the dynamics of soft robots, we deployed a neural network to learn the relationship between the actuation pattern and the target landing position, i.e., the direct model of the task. Then, a reinforcement learning method is used to predict the actuation pattern given the goal position.
