Biomimetic machines able to integrate with natural and social environments will find ubiquitous applications, from biodiversity conservation to elderly daily care. Although artificial actuators have reached the contraction performances of muscles, the versatility and grace of the movements realized by the complex arrangements of muscles remain largely unmatched. Here, we present a class of pneumatic artificial muscles, named GeometRy-based Actuators that Contract and Elongate (GRACE). The GRACEs consist of a single-material pleated membrane and do not need any strain-limiting elements. They can contract and extend by design, as described by a mathematical model, and can be realized at different dimensional scales and with different materials and mechanical performances, enabling a wide range of lifelike movements. The GRACEs can be fabricated through low-cost additive manufacturing and even built directly within functional devices, such as a pneumatic artificial hand that is fully three-dimensionally printed in one step. This makes the prototyping and fabrication of pneumatic artificial muscle–based devices faster and more straightforward.

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.

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.

We present OpenFish: an open source soft robotic fish which is optimized for speed and efficiency. The soft robotic fish uses a combination of an active and passive tail segment to accurately mimic the thunniform swimming mode. Through the implementation of a novel propulsion system that is capable of achieving higher oscillation frequencies with a more sinusoidal waveform, the open source soft robotic fish achieves a top speed of $0.85\mathrm{m}/\mathrm{s}$. Hereby, it outperforms the previously reported fastest soft robotic fish by $27\%$. Besides the propulsion system, the optimization of the fish morphology played a crucial role in achieving this speed. In this work, a detailed description of the design, construction and customization of the soft robotic fish is presented. Hereby, we hope this open source design will accelerate future research and developments in soft robotic fish.

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.

Advancements in smart soft materials are enhancing the capabilities of robotic manipulators in object interactions and complex tasks. Mechanochromic materials, acting as lightweight sensors, offer easily interpretable visual feedback for localized stress detection, structural health monitoring, and energy‐efficient robotic skins. Herein, an innovative mechanochromic soft end‐effector capable of discerning local contact stresses during mechanical interactions with objects is presented and their relative position is ascertained. This system utilizes a reversible force‐induced color switch in a thin layer of spiropyran‐functionalized polydimethylsiloxane, which coats a silicone‐made suction cup. The mechanochromic suction cup is integrated with a 3D‐printed compact load‐transferring system and electronic color‐changing detection elements. The assembly may serve as a synthetic receptor for robotic actuators, discerning localized interaction forces down to 3 N. The system's resilience to varying environmental factors, including illumination, tilting, and interaction with objects of various shapes is verified. The results indicate potential for exteroceptive solutions in reconfigurable manipulation tasks without compromising the overall softness of the manipulator.