Engineers frequently aim to streamline environmental factors to facilitate the effective operation of robots. However, in nature, environmental considerations play a crucial role in shaping the embodiment of organisms. To comply robots with the complexity of real-world environments, embedding similar intelligence is key. In the field of soft robotics, various approaches offer insight into how intelligence can be integrated into artificial agents. A discussed topic is the intricate relationship between the brain and the body at the core of intelligence in robots. The goal of this article is, therefore, to unravel the strategies to implement different types of intelligence currently adopted in soft robots. A classification is made by making a distinction between agents that adapt to their environment by 1) their adaptive shape, 2) their adaptive functionality, and 3) their adaptive mechanics. Additionally, the perspectives on intelligence based on their computational approach are distinguished: centralized computation, decentralized computation, or embedded computation. It is concluded that a tailored robotic design approach attuned to specific environmental demands is needed. To unlock the full potential of soft robots, a fresh perspective on embodied intelligence is described, so-called mechanical intelligence, emphasizing the robot's responsiveness to changing external conditions of a real-world environment.@en

The suckers on the octopus arm play a pivotal role in the execution of tasks in unstructured environments by providing a means to grip objects as well as perceive the environment through (chemo-)tactile receptors in the suckers. This work presents an octopus-inspired suction cup with high-resolution tactile sensing capabilities using a camera that captures the displacement of markers that are integrated in the suction cup. The orientation of the suction cup with respect to an object surface could be predicted with an average error of 1.97° for latitude and 9.41° for longitude. In a closed-loop control experiment, the orientation of the suction cup with respect to the object surface is estimated by an initial touch and the suction cup is consequently reoriented to approach the object surface in a perpendicular manner. The passive compliance of the suction cup is sufficient to compensate for the prediction error and a seal could be created on all of the objects. In combination with the automated design and manufacturing process, this is a major step toward the deployment of sensory innervated suction cups for motion planning and control of soft continuum robot arms.

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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.

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