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Abstract Fully soft bistable mechanisms have shown extensive applications ranging from soft robotics, wearable devices, and medical tools, to energy harvesting. However, the lack of design and fabrication methods that are easy and potentially scalable limits their further adoption into mainstream applications. Herein, a top–down planar approach is presented by introducing Kirigami‐inspired engineering combined with a pre‐stretching process. Using this method, Kirigami‐Pre‐stretched Substrate‐Kirigami trilayered precursors are created in a planar manner; upon release, the strain mismatch—due to the pre‐stretching of substrate—between layers will induce an out‐of‐plane buckling to achieve targeted 3D bistable structures. By combining experimental characterization, analytical modeling, and finite element simulation, the effect of the pattern size of Kirigami layers and pre‐stretching on the geometry and stability of resulting 3D composites is explored. In addition, methods to realize soft bistable structures with arbitrary shapes and soft composites with multistable configurations are investigated, which may encourage further applications. This method is demonstrated by using bistable soft Kirigami composites to construct two soft machines: (i) a bistable soft gripper that can gently grasp delicate objects with different shapes and sizes and (ii) a flytrap‐inspired robot that can autonomously detect and capture objects. 

Deformable linear objects (DLOs), such as rods, cables, and ropes, play important roles in daily life. However, manipulation of DLOs is challenging as large geometrically nonlinear deformations may occur during the manipulation process. This problem is made even more difficult as the different deformation modes (e.g., stretching, bending, and twisting) may result in elastic instabilities during manipulation. In this paper, we formulate a physics-guided data-driven method to solve a challenging manipulation task—accurately deploying a DLO (an elastic rod) onto a rigid substrate along various prescribed patterns. Our framework combines machine learning, scaling analysis, and physical simulations to develop a physics-based neural controller for deployment. We explore the complex interplay between the gravitational and elastic energies of the manipulated DLO and obtain a control method for DLO deployment that is robust against friction and material properties. Out of the numerous geometrical and material properties of the rod and substrate, we show that only three non-dimensional parameters are needed to describe the deployment process with physical analysis. Therefore, the essence of the controlling law for the manipulation task can be constructed with a low-dimensional model, drastically increasing the computation speed. The effectiveness of our optimal control scheme is shown through a comprehensive robotic case study comparing against a heuristic control method for deploying rods for a wide variety of patterns. In addition to this, we also showcase the practicality of our control scheme by having a robot accomplish challenging high-level tasks such as mimicking human handwriting, cable placement, and tying knots.