Learning-Based Inverse Dynamic Controller for Throwing Tasks with a Soft Robotic Arm

Controlling a soft robot poses a challenge due to its mechanical characteristics. Although the manufacturing process is well-established, there are still shortcomings in their control, which often limits them to static tasks. In this study, we aim to address some of these limitations by introducing a neural network-based controller specifically designed for the throwing task using a soft robotic arm. Drawing inspiration from previous research, we have devised a method for controlling the movement of the soft robotic arm during the ballistic task. By employing a feed-forward neural network, we approximate the relationship between the actuation pattern and the resulting landing position. This enables us to predict the input sequence that needs to be transmitted to the robot’s actuators based on the desired landing coordinates. To validate our approach, we conducted experiments using a 2-module soft robotic arm, which was utilized to throw four different objects towards ten target boxes positioned beneath the robot. We considered two actuation modalities, depending on whether the distal module was activated. The results indicate a success rate, defined as the proportion of successful trials out of the total number of throws, of up to 68% when a single module was actuated. These findings demonstrate the potential of our proposed controller in achieving successful performance of the throwing task using a soft robotic arm.