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Abstract A major intent of scientific research is the replication of the behaviour observed in natural spaces. In robotics, these can be through biomimetic movements in devices and inspiration from diverse actions in nature, also known as bioinspired features. An interesting pathway enabling both features is the fabrication of soft actuators. Specifically, 3D‐printing has been explored as a potential approach for the development of biomimetic and bioinspired soft actuators. The extent of this method is highlighted through the large array of applications and techniques used to create these devices, as applications from the movement of fern trees to contraction in organs are explored. In this review, different 3D‐printing fabrication methods, materials, and types of soft actuators, and their respective applications are discussed in depth. Finally, the extent of their use for present operations and future technological advances are discussed. 

Abstract The development of a general‐purpose machine learning algorithm capable of quickly identifying optimal 3D‐printing settings can save manufacturing time and cost, reduce labor intensity, and improve the quality of 3D‐printed objects. Existing methods have limitations which focus on overall performance or one specific aspect of 3D‐printing quality. Here, for addressing the limitations, a multi‐objective Bayesian Optimization (BO) approach which uses a general‐purpose algorithm to optimize the black‐box functions is demonstrated and identifies the optimal input parameters of direct ink writing for 3D‐printing different presurgical organ models with intricate geometry. The BO approach enhances the 3D‐printing efficiency to achieve the best possible printed object quality while simultaneously addressing the inherent trade‐offs from the process of pursuing ideal outcomes relevant to requirements from practitioners. The BO approach also enables us to effectively explore 3D‐printing inputs inclusive of layer height, nozzle travel speed, and dispensing pressure, as well as visualize the trade‐offs between each set of 3D‐printing inputs in terms of the output objectives which consist of time, porosity, and geometry precisions through the Pareto front.