This article tackles the problem of controlling articulated soft robots (ASRs), i.e., robots with either fixed or variable elasticity lumped at the joints. Classic control schemes rely on high-authority feedback actions, which have the drawback of altering the desired robot softness. The problem of accurate control of ASRs, without altering their inherent stiffness, is particularly challenging because of their complex and hard-to-model nonlinear dynamics. Leveraging a learned anticipatory action, iterative learning control (ILC) strategies do not suffer from these issues. Recently, ILC was adopted to perform position control of ASRs. However, the limitation of position-based ILC in controlling variable stiffness robots is that whenever the robot stiffness profile is changed, a different input action has to be learned. Our first contribution is to identify a wide class of ASRs, whose motion and stiffness adjusting dynamics can be proved to be decoupled. This class is described by two properties that we define: strong elastic coupling, relative to motors and links of the system and their connections; and homogeneity, relative to the characteristics of the motors. Furthermore, we design a torque-based ILC scheme that, starting from a rough estimation of the system parameters, refines the torque needed for the joint positions tracking. The resulting control scheme requires minimum knowledge of the system. Experiments on variable stiffness robots prove that the method effectively generalizes the iterative procedure with respect to the desired stiffness profile and allows good tracking performance. Finally, potential restrictions of the method, e.g., caused by friction phenomena, are discussed.

Operations involving safe interactions in unstructured environments require robots with adapting behaviors. Compliant manipulators are a promising technology to achieve this goal. Despite that, some classical control problems such as following a trajectory are still open. A typical solution is to compensate the system dynamics with feedback loops. However, this solution increases the effective robot stiffness and jeopardizes the safety property provided by the compliant design. On the other hand, purely feedforward approaches can achieve good tracking performance while preserving the robot intrinsic compliance. However, a feedforward control framework for robots with passive elastic joints is still missing. This article presents an iterative learning control algorithm for purely feedforward trajectory tracking for compliant underactuated arms. Each arm is composed of active elastic joints and a generic number of passive ones connected through rigid links. We prove the convergence of the iterative method, also in the presence of uncertainties and bounded disturbances. Different output functions are analyzed providing conditions, based on the system inertial properties that ensure the algorithm applicability. Additionally, an automatic selection of the learning gain is proposed. Finally, we extensively validate the theoretical results with simulations and experiments.

To face the demand for applications in which robots have to safely interact with humans and the environment, the research community developed new types of actuators with compliant characteristics. To embody compliance into the actuator, elastic elements with fixed or variable compliance can be used. Among the variable stiffness mechanisms, a popular approach is based on the agonistic–antagonistic (A-A) layout, where two prime movers are elastically connected to the output shaft of the actuator. Notwithstanding the conceptually simple realization of the A-A layout, one limitation is that, due to the nonlinear torque/deflection characteristic of the elastic transmissions and to the limited spring elongation, the stiffness range achievable at the output shaft reduces as the external torque increases. In this article, a novel layout, based on the A-A principle, is proposed to increase the torque/stiffness capability of the actuator. To achieve this result, we combine elastic transmissions with linear and nonlinear torque/deflection characteristics. The mathematical model of the new layout and a possible implementation are analyzed. Then, the design of a novel variable stiffness actuator is presented and experimental validations are shown to compare the new device with the benchmark.

The regulation of the link positions of compliant robots, damping out undesired link oscillations while preserving the system's inherent elasticity is still a challenging task in practical applications. This task becomes even harder to be tackled in the case of compliant robots driven by agonistic-antagonistic variable stiffness actuators in which there are two motors associated with each joint of the system. In this work, leveraging on the physical realization of the elastic mechanism of such actuators, we propose a novel control law able to simultaneously achieve a good regulation performance and a desired damped behavior at the link, while preserving the elastic structure of the system as well as the possibility of adjusting the passive stiffness at the joints. Simulations on the agonistic-antagonistic actuators of the forearm and wrist joints of the Hand Arm System from the German Aerospace Center (DLR) and experiments on a planar platform with an analogous actuation unit, namely qbMove Advanced, validate the proposed method.

To enable the design of planning and control strategies in simulated environments before their direct application to the real robot, exploiting the Sim2Real practice, powerful and realistic dynamic simulation tools have been proposed, e.g., the ROS-Gazebo framework. However, the majority of such simulators do not account for some of the properties of recently developed advanced systems, e.g., dynamic elastic behaviors shown by all those robots that purposely incorporate compliant elements into their actuators, the so-called Articulated Soft Robots ASRs. This paper presents an open-source ROS-Gazebo toolbox for simulating ASRs equipped with the aforementioned types of compliant actuators. To achieve this result, the toolbox consists of two ROS-Gazebo modules: a plugin that implements the custom compliant characteristics of a given actuator and simulates the internal motor dynamics, and a Robotic Operation System (ROS) manager node used to organize and simplify the overall toolbox usage. The toolbox can implement different compliant joint structures to perform realistic and representative simulations of ASRs, also when they interact with the environment. The simulated ASRs can be also used to retrieve information about the physical behavior of the real system from its simulation, and to develop control policies that can be transferred back to the real world, leveraging the Sim2Real practice. To assess the versatility of the proposed plugin, we report simulations of different compliant actuators. Then, to show the reliability of the simulated results, we present experiments executed on two ASRs and compare the performance of the real hardware with the simulations. Finally, to validate the toolbox effectiveness for Sim2Real control design, we learn a control policy in simulation, then feed it to the real system in feed-forward comparing the results.