Fully exploiting soft robots' capabilities requires devising strategies that can accurately control their movements with the limited amount of control sources available. This task is challenging for reasons including the hard-to-model dynamics, the system's underactuation, and the need of using a prominent feedforward control action to preserve the soft and safe robot behavior. To tackle this challenge, this letter proposes a purely feedforward iterative learning control algorithm that refines the torque action by leveraging both the knowledge of the model and data obtained from past experience. After presenting a 3D polynomial description of soft robots, we study their intrinsic properties, e.g., input-to-state stability, and we prove the convergence of the controller coping with locally Lipschitz nonlinearities. Finally, we validate the proposed approach through simulations and experiments involving multiple systems, trajectories, and in the case of external disturbances and model mismatches.@en

Soft robots enable safe and robust operations in unstructured environments. However, the nonlinearities of their continuum structure complicate the accomplishment of classic robotic tasks, such as pick and place. In this work, we propose the R-Soft Inverted Pendulum, a Soft Inverted Pendulum (SIP) actuated only by a revolute joint at the base. The objective is to exploit the snap effect to execute pick and place task. We model the proposed system with two approaches: Curvature Parametrization and Strain Parametrization. The former is particularly simple and easy to implement in the classic dynamics of a rigid manipulator, although it suffers from numerical issues. The latter is more complex but guarantees numerical robustness. Additionally, we analyze the equilibria of the system and its structural properties. Furthermore, we propose a control law based on feedback linearization. Finally, we validate the proposed system through simulations.@en