Soft robots offer a number of advantages over traditional rigid robots in applications such as minimally invasive surgery, where safety and dexterity are required. In previous works, the STIFF-FLOP manipulator has been introduced as a new concept of using soft materials to develop endoscopic tools with high dexterity and intrinsic safety. However, due to its inherent low stiffness, the ability to generate higher forces and stability when required remains to be further explored. In the state-of-the-art technology, there is no optimal solution that satisfies all the desired requirements in terms of miniaturized dimensions, free lumen for passing tools up to the tip, stiffness variation, and dexterity. In this paper, we compare different variable stiffness technologies and present a novel design that comprises a stiffening system based either on a fiber jamming (FJ) transition or low-melting-point alloys (LMPAs) that can be embedded in the manipulator to widen its applicability by increasing its stability and load bearing capability. The two approaches have been evaluated and compared in terms of variable stiffness capability and dexterity. The results suggest that the LMPA-based solution significantly outperforms previous approaches using similar designs with a higher stiffness variation combined with a good degree of flexibility, while the solution based on FJ guarantees fast transition times and fully satisfies the required safety measures.

Soft robotic grippers enable the safe manipulation of delicate objects, guaranteeing their integrity when handled and collected. Integrating sensors into these grippers can enable their proprioception but must avoid compromising flexibility or functionality. This study presents a pneumatic finger‐based soft gripper with a novel piezoresistive sensor made of laser‐induced graphene (LIG) embedded in dragon skin (DS), an elastomer matrix, offering continuous bending angle measurement. The LIG/DS composite is studied to confirm minimal impact on the gripper's stiffness. Mechanical and electromechanical characterizations are performed for two sensor designs, n ₁ and n ₂ . Design n ₁ exhibits superior performance, with a gauge factor , a linear response of up to 30% strain, and durability exceeding 10 000 cycles. A finite‐element method analysis identifies the fingers’ neutral bending plane, guiding optimal sensor placement. Experimental validation confirms theoretical predictions and finds the ideal sensor location, achieving a linear response up to 110° with low hysteresis (8%). The sensor enables real‐time monitoring of finger bending during grasping tasks, with a calibration curve linking resistance changes to bending angles. This cost‐effective, stretchable, and durable sensor demonstrates high potential for soft robotic applications, offering precise and reliable proprioception without compromising the gripper's soft properties.

Soft robots are promising in biomedical applications thanks to their inherent structural compliance and distributed large deformations. However, integrating a sensory system that maintains the robot's dexterity while offering accurate state estimation remains an open challenge for their widespread adoption. This letter presents SoftTex, a small-scale soft robotic arm built with textile fabrics. SoftTex redesigns the STIFF-FLOP soft manipulator to favor affordable, rapid, and repeatable fabrication methods, facilitating the integration of a proprioceptive system based on piezoresistive textile strips preserving the soft arm compliance. First, we characterized the bending and stretching capabilities of the soft robotic arm and its workspace. The force tests demonstrated effectiveness for potential biomedical applications, revealing pulling forces ranging from 3.4–7.4 N and pushing forces from 2–7.5 N. Finally, we leveraged actuation, motion, and proprioceptive data collected with an open-loop controller to develop a position state estimator using a parallel recurrent neural network trained with supervised curriculum learning. The proprioceptive network achieves an average prediction error of ${\text{2.0}} \pm {\text{1.8}}$ mm ($3.4 \pm 2.9\%L$, where $L$ is module length). The findings are promising for closed-loop control, addressing the demand for low-cost, sensor-equipped soft robotic arms in the medical field and enhancing their potential for confined space exploration.

There has been a notable focus on the adoption of jamming-based technologies, which involve increasing the friction between grains, layers, or fibers to achieve variable stiffness capability in soft robots. Additionally, magnetorheological elastomers (MREs) that show magnetic-field-dependent viscoelasticity have great potential as a material for varying stiffness. This study proposes a hybrid method (magnetic jamming of MRE fibers) for enhancing the stiffness of soft robots, combining a jamming-based with a viscosity-based method. First, a fiber jamming structure is developed and integrated into a soft robot, the STIFF-FLOP manipulator, to prove the concept of the magnetic jamming of MRE fibers. Then, based on the proposed method, a variable stiffness device actuated by electro-permanent magnets is developed. The device is integrated into the same manipulator and the electronically controlled magnetic jamming and stiffening of the manipulator is demonstrated. The experimental results show that stiffness gain in bending and compression is achieved with the proposed method. The outcomes of this investigation demonstrate that the proposed hybrid stiffening technique presents a promising avenue for realizing variable and controllable stiffness in soft robots.

Fingerprints primarily enhance grasping by modulating friction, as the surface pattern affects the friction needed for effective grasping and manipulation. A versatile gripper was developed with a more realistic distal phalange inspired by the human index finger for improved berry gripping performance. The soft pneumatic gripper was fabricated out of a Diels-Alder–based self-healing polymer, leveraging its thermally reversible nature for introducing interchangeable fingertips. Various fingertips were developed with different patterns for optimized grasping of fruits of different shape, size, and stiffness, depending on the type of fruit, level of ripeness, and variations in weather and climate conditions of each harvest season. The fingertips can be mounted and removed easily from the distal phalange of the pneumatic finger by welding and manual disassembly. The gripper was optimized for picking raspberries, exploiting the innovative interchangeable fingertips. The same gripper could successfully harvest berries of different levels of ripeness thanks to three types of interchangeable fingertips with different patterns. The performance of the different fingertips was evaluated in the lab on a physical twin of a raspberry and a 67% success rate was obtained in the field by switching them on the spot to optimize the grasp to the different ripeness level of the berries. The GraspBerry paves the way for the design of versatile grippers with adaptable grasping performance by switching the fingertips for accurate and reliable grasp on different types of crops throughout the various harvesting seasons all year round.

Variable stiffness is typically employed in soft robotics to address the trade-off between compliance and the ability to generate stability when required. Among the several approaches investigated, jamming transition systems show remarkable stiffness performance and fast response. Building upon the preliminary study on a seashell bioinspired variable stiffness structure, here we extend the design space through a parametric study supported by a finite element model based on commercially available software. The study allows establishing the relationship between the design parameters and the stiffness performance. Moreover, the optimal configuration in terms of performance to energy consumption is identified and compared to previous similar approaches. Finally, the low computational cost of the finite element model demonstrated to be an effective tool for the analysis of complex geometries, thereby establishing a foundation for the development of cost-effective and lightweight soft robotic devices empowered by variable stiffness capabilities (e.g. a wearable device for assistance).