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Abstract Photocatalysis is an attractive, energy-efficient technology for organic transformations, polymer synthesis, and degradation of environmental pollutants. There is a need for new photocatalysts stable in different media and that can be tailored for specific applications. Covalent organic frameworks (COF) are crystalline, nanoporous materials withπ-conjugated backbone monomers, representing versatile platforms as heterogeneous, metal-free photocatalysts. The backbone structure can be tailored to achieve desired photocatalytic properties, side-chains can mediate adsorption, and the nanoporous structure provides large surface area for molecular adsorption. While these properties make COFs attractive as photocatalysts, several fundamental questions remain regarding mechanisms for different photocatalytic transformations, reactant transport into porous COF structures, and both structural and chemical stability in various environments. In this perspective, we provide a brief overview of COF photocatalysts and identify challenges that should be addressed in future research seeking to employ COFs as photocatalysts. We close with an outlook and perspective on future research directions in the area of COF photocatalysts. 

Light‐responsive materials enable the development of soft robots that are controlled remotely in 3D space and time without the need for cumbersome wires, onboard batteries, or altering the local environment. Azobenzene liquid crystal polymer networks are one such material that can move and deform in response to light actuation. Previous works have demonstrated azo‐based soft robotic grippers and transporters that are remotely powered by light. However, highly adaptive, automated spatiotemporal optical control over these materials has not yet been realized. Herein, a system for an azobenzene liquid crystal elastomer soft robotic arm is created by dynamically patterning light for independently maneuverable joints. The nonlinear material response to optical actuation is characterized, and the broad actuation space is explored with diverse arm configurations. A neural network is trained on the arm configurations and corresponding laser pattern to automate the pattern generation for a desired configuration. Finally, the azobenzene liquid crystal elastomer arm demonstrates complex targeted motion, marking an important step toward optically actuated soft robotics with applications ranging from optomechanics to biomedical tools.