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The fully automated fabrication of robots has long been a holy grail with the potential to revolutionize various industries, including manufacturing, construction, disaster relief, and space exploration. 3D printing offers a promising approach to automation, but the ability to print entire, complex robots with multiple materials remains limited. Previous approaches have simplified robot manufacturing by using fluidic control circuits, but these rely on labor‐intensive methods like silicone molding and manual assembly, limiting accessibility and replicability. Recent work, including this work, has demonstrated 3D‐printed robotic grippers and crawlers with embedded control circuits, but generating cyclic control outputs for legged locomotion in rough terrain remains challenging. This study addresses the challenge with a monolithic 3D‐printable four‐phase bistable oscillating valve, capable of generating coordinated motion of multiple limbs from a steady source of pressurized air. The ability of the oscillator to control an electronics‐free autonomous legged robot capable of walking on rough terrain, which can be fully fabricated on a desktop 3D printer without postassembly is demonstrated. The robot is operational immediately upon connection to an air supply. This development marks a significant step toward accessible, customizable, and biodegradable autonomous soft robots that can be produced using desktop 3D printers with no human intervention. 

Concentric tube robots (CTRs) show particular promise for minimally invasive surgery due to their inherent compliance and ability to navigate in constrained environments. Due to variations in anatomy among patients and variations in task requirements among procedures, it is necessary to customize the design of these robots on a patient- or population-specific basis. However, the complex kinematics and large design space make the design problem challenging. Here we propose a computational framework that can efficiently optimize a robot design and a motion plan to enable safe navigation through the patient’s anatomy. The current framework is the first fully gradient-based method for CTR design optimization and motion planning, enabling an efficient and scalable solution for simultaneously optimizing continuous variables, even across multiple anatomies. The framework is demonstrated using two clinical examples, laryngoscopy and heart biopsy, where the optimization problems are solved for a single patient and across multiple patients, respectively.