Air tightness is a challenging task for 3D-printed components, especially for fused filament fabrication (FFF), due to inherent issues, related to the layer-by-layer fabrication method. On the other hand, the capability of 3D print airtight cavities with complex shapes is very attractive for several emerging research fields, such as soft robotics. The present paper proposes a repeatable methodology to 3D print airtight soft actuators with embedded air connectors. The FFF process has been optimized to manufacture monolithic bending PneuNets (MBPs), an emerging class of soft robots. FFF has several advantages in soft robot fabrication: (i) it is a fully automated process which does not require manual tasks as for molding, (ii) it is one of the most ubiquitous and inexpensive (FFF 3D printers costs < $200) 3D-printing technologies, and (iii) more materials can be used in the same printing cycle which allows embedding of several elements in the soft robot body. Using commercial soft filaments and a dual-extruder 3D printer, at first, a novel air connector which can be easily embedded in each soft robot, made via FFF technology with a single printing cycle, has been fabricated and tested. This new embedded air connector (EAC) prevents air leaks at the interface between pneumatic pipe and soft robot and replaces the commercial air connections, often origin of leakages in soft robots. A subsequent experimental study using four different shapes of MBPs, each equipped with EAC, showed the way in which different design configurations can affect bending performance. By focusing on the best performing shape, among the tested ones, the authors studied the relationship between bending performance and air tightness, proving how the Design for Additive Manufacturing approach is essential for advanced applications involving FFF. In particular, the relationship between chamber wall thickness and printing parameters has been analyzed, the thickness of the walls has been studied from 1.6 to 1 mm while maintaining air tightness and improving the bending angle by 76.7% under a pressure of 4 bar. It emerged that the main printing parameter affecting chamber wall air tightness is the line width that, in conjunction with the wall thickness, can ensure air tightness of the soft actuator body.

Nowadays, a challenging scenario involving additive manufacturing (AM), or 3D printing, relates to concerns on the manufacturing of electronic devices. In particular, the possibility of using fused filament fabrication (FFF) technology, which is well known for being very widespread and inexpensive, to fabricate structures with embedded sensing elements, is really appealing. Several researchers in this field have highlighted the high electrical resistance values and variability in 3D-printed strain sensors made via FFF. It is important to find a way to minimize the electrical resistance and variability among strain sensors printed under the same conditions for several reasons, such as reducing the measurement noise and better balancing four 3D-printed strain gauges connected to form a Wheatstone bridge to obtain better measurements. In this study, a design of experiment (DoE) on 3D-printed strain gauges, studying the relevance of printing and design parameters, was performed. Three different commercial conductive materials were analyzed, including a total of 105 printed samples. The output of this study is a combination of parameters which allow both the electrical resistance and variability to be minimized; in particular, it was discovered that the “welding effect” due to the layer height and printing orientation is responsible for high values of resistance and variability. After the optimization of printing and design parameters, further experiments were performed to characterize the sensitivity of each specimen to mechanical and thermal stresses, highlighting an interesting aspect. A sensible variation of the electrical resistance at room temperature was observed, even if no stress was applied to the specimen, suggesting the potential of exploiting these materials for the 3D printing of highly sensitive temperature sensors.

Soft robots can adaptively interact with unstructured environments. However, nonlinear soft material properties challenge modeling and control. Learning‐based controllers that leverage efficient mechanical models are promising for solving complex interaction tasks. This article develops a closed‐loop pose/force controller for a dexterous soft manipulator enabling dynamic pushing tasks using deep reinforcement learning. Force tests investigate the mechanical properties of a soft robot module, resulting in orthogonal forces of N. Then, the policy is trained in simulation leveraging a dynamic Cosserat rod model of the soft robot. Domain randomization mitigate the sim‐to‐real gap while careful reward engineering induced pose and force control even without explicit force inputs. Despite the approximate simulation, the sim‐to‐real transfer achieved an average reaching distance of mm (), an average orientation error of rad () and applied pushing forces up to N. Such performance is reasonable for the intended assistive tasks of the manipulator. The experiments uncovered that the soft robot interacting with the environment exhibited torsional and counter‐balancing movements. Although not explicitly enforced, they emerged from the mechanical intelligence of the manipulator. The results demonstrate the potential of soft robotic manipulation via reinforcement learning.

Additive manufacturing technologies allow the fabrication of smart objects, which are made up of a dielectric part and an embedded sensor able to give real-time feedback to the final user. This research presents the characterization of a low-cost 3D-printed strain sensor, fabricated using material extrusion (MeX) technology by using a conductive material composed of a polylactic acid (PLA)-based matrix doped with carbon black and carbon nanotubes (CNT), thus making the plastic conductive. A suitable measurement set-up was developed to perform automatic characterization tests using a high repeatability industrial robot to define either displacement or force profiles. The correlation between the applied stimulus and the variation of the electrical resistance of the 3D-printed sensor was evaluated, and an approach was developed to compensate for the effect of temperature. Results show that temperature and hysteresis affect repeatability; nevertheless, the sensor accurately detects impulse forces ranging from 10 g to 50 g. The sensor showed high linearity and exhibited a sensitivity of $0.077{\mathsf{\Omega }\mathrm{g}}^{-1}$ and $12.54 {\mathsf{\Omega }\mathrm{mm}}^{-1}$ in the force and displacement range of 114 g and 0.7 mm, respectively, making them promising due to their low cost, ease of fabrication, and possible integration into more complex devices in a single-step fabrication cycle.

The fabrication of bioinspired structures has recently gained an increasing popularity: mimicking the way in which nature develops structures is a vital prerequisite in soft robotics to achieve multiple benefits. Stiff structures connected by soft joints (recalling, for instance, human bones connected by cartilage) are highly appealing: several prototypes have been manufactured and tested, demonstrating their full potential. In the present research, the material extrusion (MEX) additive manufacturing technology has been used to manufacture stiff–soft bioinspired structures activated by shape memory alloy (SMA) actuators. First, three commercially available stiff composite plastic materials were investigated and linked to different 3D printing infills. Surprisingly, we found that the “gyroid” infill was correlated to the mechanical properties, demonstrating that it produces better results in terms of Young's modulus and ultimate tensile strength (UTS) than the widely studied “lines” infill. The primary focus of the research is an experimental study aimed at improving the adhesion at the interface between stiff and soft materials using an inexpensive method (i.e., MEX). Three different variables that have significant effects on the interface bonding were studied: (1) the interface geometry between stiff and soft parts, (2) the mesh overlapping process parameter, and (3) the annealing post-treatment. By optimizing the three variables, a Young's modulus of 48.8 MPa and a UTS of 3.8 MPa were achieved, when nylon+glass fiber (a stiff material) and thermoplastic polyurethane (a soft material) were 3D printed together. In particular, the 3.8 MPa UTS is 48% higher than the highest adhesion between the soft and stiff material (thermoplastic polyurethane [TPU] and acrylonitrile butadiene styrene) reported in literature. Finally, taking advantage of the improved stiff–soft adhesion, a bioinspired robotic finger has been fabricated and tested using an SMA actuator, showing an enormous potential for the proposed additive manufacturing approach in realizing bioinspired systems.