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.

The octopus has unique capacities are sources of inspiration in developing soft robotic‐enabling technologies. Herein, soft, sensorized, suction cups inspired by the suckers of Octopus vulgaris are presented. The suction cups using direct casting are fabricated, so that materials with different mechanical properties can be combined to optimize sensing and grasping capabilities. The artificial suckers integrate four embedded strain sensors, individually characterized and placed in a 90° configuration along the rim of the suction cup. Based on this arrangement, how well the sensory suction cup can detect 1) the direction and 2) the angle (from 30° to 90°) of a touched inclined surface and 3) the stiffness of a touched flat object (shore hardness between 0010 and D50) both in air and underwater is evaluated. Data processing on neural networks is based using a multilayer perceptron to perform regression on individual properties. The results show a mean absolute error of 0.98 for angles, 0.02 for directions, and 97.9% and 93.5% of accuracy for the material classification in air and underwater, respectively. In view of the results and scalability in manufacturing, the proposed artificial suckers would seem to be highly effective solutions for soft robotics, including blind exploration and object recognition.

Asglobal demand for fruits and vegetables continues to rise, the agricultural industry faces significant challenges in securing adequate labor. Robotic harvesting devices offer a promising solution to address this issue. Harvesting delicate fruits, such as blackberries, presents unique open challenges due to their fragility. This letter introduces BerryTwist, a prototype robotic gripper specifically designed for blackberry harvesting. The gripper features a fabric tube mechanism that uses motorized twisting action to gently envelop the fruit, ensuring uniform pressure application and minimizing damage. The twisting motion is transferred to the tube through a compliant mechanism, thus maintaining the overall softness of the structure. We thoroughly tested BerryTwist, paying particular attention to the effect of varying tube properties. We developed three types of tubes varying in elasticity and compressibility, using foam padding, spandex, and food-safe cotton cheesecloth. Performance testing focused on assessing each gripper's ability to detach and release blackberries, with an emphasis on quantifying damage rates. The results indicate that the proposed gripper achieved an 82% success rate in detaching blackberries and a 95% success rate in releasing them, demonstrating its promising potential for robotic harvesting applications. Finally, we will demonstrate the robotic harvesting operation by establishing a simple farm setup and integrating the gripper with Franka Emika's robot manipulator.

Soft pneumatic network (Pneu-net) actuators are frequently used to achieve sophisticated movements, but they face challenges in producing both bending and twisting motions concurrently. In this paper, we present a new Pneu-net twisting and bending actuator (PTBA) design that enables them to perform complex motions. We achieved this by adjusting the chamber angle, ranging from 15 to 75 degrees, to optimize the bending and twisting movements through finite element analysis and experimental verification. We also investigated the variation trends in bending and twisting motions and determined the actuator’s workspace and maximum grasping force for a variety of objects with different shapes, materials, and sizes. Our findings suggest that PTBA is a promising candidate for advanced applications requiring intricate and bioinspired movements. This new design method offers a path toward achieving these goals.

Octopuses exhibit remarkable motor dexterity through distributed sensing and control in their flexible arms. Inspired by this biological model, we present a tendon-driven soft robotic arm featuring optoelectronic mechanosensors embedded in suction cups and a hierarchical behaviour-based control architecture. Each artificial sucker integrates light-emitting diodes and phototransistors to detect contact force and direction via light reflection, achieving high sensitivity (~400 mV N ⁻¹ in the 0–2-N range) and directional accuracy (error, <18°). The sensors operate reliably in dry and wet environments with minimal drift and hysteresis. Their compact, modular design facilitates system integration. The hierarchical control architecture enables local reflexes at the suction cup level and global coordination for autonomous grasping. The system reliably detects contact events, estimates force and direction and infers object position relative to the arm, enabling purposeful interaction. This work advances sensor-integrated soft robotics, demonstrating the potential of biologically inspired designs for adaptive grasping in unstructured environments.