The emergence of the field of soft robotics has led to an interest in suction cups as auxiliary structures on soft continuum arms to support the execution of manipulation tasks. This application poses demanding requirements on suction cups with respect to sensorization, adhesion under non-ideal contact conditions, and integration into fully soft systems. The octopus can serve as an important source of inspiration for addressing these challenges. This review aims to accelerate research in octopus-inspired suction cups by providing a detailed analysis of the octopus sucker, determining meaningful performance metrics for suction cups on the basis of this analysis, and evaluating the state-of-the-art in suction cups according to these performance metrics. In total, 47 records describing suction cups are found, classified according to the deployed actuation method, and evaluated on performance metrics reflecting the level of sensorization, adhesion, and integration. Despite significant advances in recent years, the octopus sucker outperforms all suction cups on all performance metrics. The realization of high resolution tactile sensing in suction cups and the integration of such sensorized suction cups in soft continuum structures are identified as two major hurdles toward the realization of octopus-inspired manipulation strategies in soft continuum robot arms.

Sitting comfort is an important factor for passengers in selecting cars, airlines, etc. This paper proposes a soft robotic module that can be integrated into the seat cushion to provide better comfort experiences to passengers. Building on rapid manufacturing technologies and a data-driven approach, the module can be controlled to sense the applied force and the displacement of the top surface and actuate according to four designed modes. A total of 2 modules were prototyped and integrated into a seat cushion, and 16 subjects were invited to test the module’s effectiveness. Experiments proved the principle by showing significant differences regarding (dis)comfort. It was concluded that the proposed soft robotics module could provide passengers with better comfort experiences by adjusting the pressure distribution of the seat as well as introducing a variation of postures relevant for prolonged sitting.

We present a novel DC motor driven soft robotic fish which is optimized for speed and efficiency based on experimental, numerical and theoretical investigation into oscillating propulsion. Our system achieves speeds up to 0.85 m/s, outperforming the previously reported fastest free swimming soft robotic fish by a significant margin of 27%. A simple and effective wire-driven active body and passive compliant body are used to mimic highly efficient thunniform swimming. The efficient DC motor to drive the system decreases internal losses compared to other soft robotic oscillating propulsion systems which are driven by one or multiple servo motors. The DC motor driven design allows for swimming at higher frequencies. The current design has been tested up to a tailbeat frequency of 5.5 Hz, and can potentially reach much higher frequencies.

Real-time proprioception is a challenging problem for soft robots, which have virtually infinite degrees of freedom in body deformation. When multiple actuators are used, it becomes more difficult as deformation can also occur on actuators caused by interaction between each other. To tackle this problem, we present a method in this article to sense and reconstruct 3-D deformation on pneumatic soft robots by first integrating multiple low-cost sensors inside the chambers of pneumatic actuators and then using machine learning to convert the captured signals into shape parameters of soft robots. An exterior motion capture system is employed to generate the datasets for both training and testing. With the help of good shape parameterization, the 3-D shape of a soft robot can be accurately reconstructed from signals obtained from multiple sensors. We demonstrate the effectiveness of this approach on two soft robot designs - a robotic joint and a deformable membrane. After parameterizing the deformation of these soft robots into compact shape parameters, we can effectively train the neural networks to reconstruct the 3-D deformation from the sensor signals. The sensing and shape prediction pipeline can run at 50 Hz in real time on a consumer-level device.

Silicones have desirable properties such as skin-safety, high temperature-resistance, and flexibility. Many applications require the presence of a hard body connected to the silicone. Traditionally, it has been difficult to create strong bonding between silicones and hard materials. In this study, a technique is presented to control the bonding strength between silicones and thermoplastics through mechanical interlocking. This is realized through a hybrid fabrication method where silicone is cast onto a 3D-printed mold and interlocking structure. The influence of the structure's design parameters on the bonding strength is explored through theoretical modeling and physical testing, while the manufacturability of the 3D-printed structure is ensured. A CAD tool is developed to automatically apply the interlocking structure to product surfaces. The user interface visualizes the theoretical strength of the cells as the designer adjusts the cell parameters, allowing the designer to iteratively optimize the structure to the product's load case. The bonding strength of the presented mechanical interlocking structure is more than 5.5 times higher than can be achieved with a commercially available primer. The presented technique enables custom digital design and manufacturing of durable free-form parts. This is demonstrated through application of the technique in over-molded products, airtight seals, and soft pneumatic actuators.

Robots fabricated with soft materials can provide higher flexibility and, thus, better safety while interacting in unpredictable situations. However, the usage of soft material makes it challenging to predict the deformation of a continuum body under actuation and, therefore, brings difficulty to the kinematic control of its movement. In this article, we present a geometry-based framework for computing the deformation of soft robots within the range of linear material elasticity. After formulating both manipulators and actuators as geometry elements, deformation can be efficiently computed by solving a constrained optimization problem. Because of its efficiency, forward and inverse kinematics for soft manipulators can be solved by an iterative algorithm with a low computational cost. Meanwhile, components with multiple materials can also be geometrically modeled in our framework with the help of a simple calibration. Numerical and physical experimental tests are conducted on soft manipulators driven by different actuators with large deformation to demonstrate the performance of our approach.

This work presents a soft robotic module that can sense and control contact forces. The module is composed of a foam spring encapsulated by a pneumatic bellow that can be inflated to increase its stiffness. Optical sensors and a light source are integrated inside the soft pneumatic module. Changes in shape of the module lead to a variation in light reflectivity, which is captured by the optical sensors. These shape measurements are combined with air pressure measurements to predict the contact force through a machine learning model. Using these predictions, a closed-loop control of the contact force was implemented. The modules can be applied to realize pressure distribution control in support devices such as seats and mattresses. The presented method is robust and low-cost, can measure both shape and contact force, and does not require (rigid) sensors to be present at the movable contact interface between the support device and the user.

Actuators using soft materials feature a large number of degrees of freedom. This tremendous flexibility allows a soft actuator to passively adapt its shape to the objects under interaction. In this paper, we propose a novel proprioception method for soft actuators during real-time interaction with previously unknown objects. First, we design a color-based sensing structure that instantly translates the inflation of a bellow into changes in color, which are subsequently detected by a miniaturized color sensor. The color sensor is small and, thus, multiple of them can be integrated into soft pneumatic actuators to reflect local deformations. Second, we make use of a feed-forward neural network to reconstruct a multivariate global shape deformation from local color signals. Our results demonstrate that deformations of the actuator during interaction, including sigmoid-like shapes, can be accurately reconstructed. The accurate shape sensing represents a significant step toward closed-loop control of soft robots in unstructured environments.

For grasping (unknown) objects, soft pneumatic actuators are primarily designed to bend towards a specific direction. Due to the flexibility of material and structure, soft actuators are also prone to out-of-plane deformations including twisting and sidewards bending, especially if the loading is asymmetric. In this paper, we demonstrate the negative effects of out-of-plane deformation on grasping. A structural design is proposed to reduce this type of deformation and thus improve grasping stability. Comparisons are first performed on soft pneumatic actuators with the same bending stiffness but different resistances to out-of-plane deformation, which is realized by changing the cross-section of the inextensible layer. To reduce out-of-plane deformation, a stiffening structure inspired by spatial flexures is integrated into the soft actuator. The integrated design is 3D printed using a single material. Physical experiments have been conducted to verify the improved grasping stability.

The need for flexible, highly sensitive tactile sensors that can fit onto curved surfaces is driving the conformable sensor materials research in the field of human–machine interactions. Here we report a new type of compliant piezoelectric active composite, a micro-porous polyurethane-PZT material, capable of generating a voltage output upon touch. The composites are synthesized with the aim of maximizing the piezoelectric sensitivity of particulate composite sensor materials. The goal is to reduce the dielectric constant of the polymer matrix and improve flexibility of conventional bulk piezo-composites, consisting of ceramic particles in a dense polymeric matrix, by adding a third (gaseous) phase to the system in the form of uniformly sized pores. The presence of the gaseous component in the polymer matrix in the form of well-distributed spherical inclusions effectively decreases the polymer dielectric permittivity, which increases the piezoelectric voltage sensitivity (g₃₃) of the composite sensors significantly. The unique combination of dielectrophoretic structuring of PZT particles and the addition of a gaseous phase to the polymer resin results in the highest performance of the particulate composite sensors reported in the literature so far. The newly developed micro-porous composites show g₃₃ value of 165 mV m/N that is twice that of the structured PZT-bulk PU composites (80 mV m/N) and more than five times the g₃₃ value of bulk PZT ceramics (24–28 mV m/N). The capability of the flexible freestanding sensors for application in touch sensing devices for soft robotics is demonstrated.