Wireless actuation by magnetic fields allows for the operation of untethered miniaturized devices,e.g.in biomedical applications. Nevertheless, generating large controlled forces over relatively large distances is challenging. Magnetic torques are easier to generate and control, but they are not always suitable for the tasks at hand. Moreover, strong magnetic fields are required to generate a sufficient torque, which are difficult to achieve with electromagnets. Here, we demonstrate a soft miniaturized actuator that transforms an externally applied magnetic torque into a controlled linear force. We report the design, fabrication and characterization of both the actuator and the magnetic field generator. We show that the magnet assembly, which is based on a set of rotating permanent magnets, can generate strong controlled oscillating fields over a relatively large workspace. The actuator, which is 3D-printed, can lift a load of more than 40 times its weight. Finally, we show that the actuator can be further miniaturized, paving the way towards strong, wirelessly powered microactuators.

Soft microrobots based on photoresponsive materials and controlled by light fields can generate a variety of different gaits. This inherent flexibility can be exploited to maximize their locomotion performance in a given environment and used to adapt them to changing conditions. Albeit, because of the lack of accurate locomotion models, and given the intrinsic variability among microrobots, analytical control design is not possible. Common data-driven approaches, on the other hand, require running prohibitive numbers of experiments and lead to very sample-specific results. Here we propose a probabilistic learning approach for light-controlled soft microrobots based on Bayesian Optimization (BO) and Gaussian Processes (GPs). The proposed approach results in a learning scheme that is data-efficient, enabling gait optimization with a limited experimental budget, and robust against differences among microrobot samples. These features are obtained by designing the learning scheme through the comparison of different GP priors and BO settings on a semi-synthetic data set. The developed learning scheme is validated in microrobot experiments, resulting in a 115% improvement in a microrobot's locomotion performance with an experimental budget of only 20 tests. These encouraging results lead the way toward self-adaptive microrobotic systems based on light-controlled soft microrobots and probabilistic learning control.

Soft materials are being adopted in robotics in order to facilitate biomedical applications and in order to achieve simpler and more capable robots. One route to simplification is to design the robot's body using `smart materials' that carry the burden of control and actuation. Metamaterials enable just such rational design of the material properties. Here we present a soft robot that exploits mechanical metamaterials for the intrinsic synchronization of two passive clutches which contact its travel surface. Doing so allows it to move through an enclosed passage with an inchworm motion propelled by a single actuator. Our soft robot consists of two 3D-printed metamaterials that implement auxetic and normal elastic properties. The design, fabrication and characterization of the metamaterials are described. In addition, a working soft robot is presented. Since the synchronization mechanism is a feature of the robot's material body, we believe that the proposed design will enable compliant and robust implementations that scale well with miniaturization.

Micro-technologies based on wirelessly powered and manoeuvred submillimeter device, i.e.,microrobots, are attracting growing attention. Their application in lab-on-a-chip systems, such as micromanipulation and in vitro cell sorting, is expected to steeply increase. However, the actuation, powering and control of microrobots are challenges that still need concrete solutions. Magnetic fields generally enable wireless navigation of microrobots, but proper control architectures and magnetic navigation systems are needed, depending on the specific task and on the level of interaction required to the user. Here we present a magnetic navigation platform intended for lab-on-a-chip applications and we address its usability with different levels of human involvement by using two control architectures: teleoperated and autonomous. We perform an experimental analysis to demonstrate that both architectures, enrolling different levels of interaction by the user, lead to reliable execution of the microrobotic task. First, we validate the open-loop response of the microrobotic system, and second, we evaluate the performance of the system by testing both control architectures with a standard mobility task. The results show that users can teleoperate the microrobot with 100% success rate, in 14.4±1.9s with a normalized spatial mean error of 0.60±0.13. Moreover, results show a fast decaying learning curve for the users involved in the study. Compared to this, when the navigation task is performed by the autonomous control, 100% success rate, a time of 8.0±0.5s and a normalized spatial mean error of 0.50±0.05 are obtained. Finally, we quantitatively demonstrate how both control methodologies enable very smooth movements of the microrobot, suggesting application for any task where repeatable and dexterous movements in liquid microenvironments are key requirements.

In this work, the design and development of an integrated platform for the steering of swimming microrobot is reported. The system consists of: a near-spherical soft and buoyant magnetic microrobot (with a diameter of about 500 μm) conceived for operation in liquid; a wireless magnetic steering system, including a compact magnetic field generator based on two pairs of Helmholtz and Maxwell coils; an electronic system for their driving; a control software; a joypad physical user interface; and, the micro-arena as working environment. The platform design fulfills the requirements for the “Mobility Task” of the 2011 NIST Mobile Microrobotics Challenge. The results obtained from preliminary validation experiments confirm that the microrobots can move in a fully controlled way, successfully accomplishing an intricate eight-shape path, as required, in the water filled micro-arena. In particular we achieved a maximum average speed of 0.71 mm/s and an exceptionally smooth motion.

A novel approach for the design of magnetically-propelled microrobots is proposed as an effective solution for swimming in a liquid medium. While intrinsic neutral buoyancy of a microrobot per se simplifies propulsion in the liquid environments, softness makes it compliant with delicate environments, such as the human body, thus guaranteeing a safe interaction with soft structures. With this aim, two groups of soft microrobots with paramagnetic and ferromagnetic behaviors were designed, fabricated and their features were experimentally analyzed. In agreement with the theoretical predictions, in the performed trials the ferromagnetic microrobots showed orientation capabilities in response to the magnetic field that could not be achieved by the paramagnetic one. Moreover, it was observed that the ferromagnetic microrobot could reach higher speed values (maximum value of 0.73 body length/s) than the paramagnetic prototype.