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Manipulating airflow is important for controlling pneumatically actuated soft robots, however, current switching techniques suffer from leakage under high pressure (>200 kPa) or require a complex fabrication process. We propose a new method for reliably and repeatably cutting off airflow by harnessing pre-loaded torsional forces applied to our tubing. The switching distance and hysteresis of our pre-twisted tubing are programmable by varying the tube length and the twisting angle. Our experiments demonstrate the use of pretwisted tubing to implement CMOS equivalent fluidic switches configured as NOT-, AND-, and OR-gates, and a distance sensor for feedback control for the oscillation of a PneuNet. Our approach of pre-loading tubes with a torsional force allows for simplicity, integrated functionality, and the capability of manipulating high-pressure, fluidic signals mainly at the cost of tubing. 

Pneumatic soft robots are typically fabricated by molding, a manual fabrication process that requires skilled labor. Additive manufacturing has the potential to break this limitation and speed up the fabrication process but struggles with consistently producing high-quality prints. We propose a low-cost approach to improve the print quality of desktop fused deposition modeling by adding a webcam to the printer to monitor the printing process and detect and correct defects such as holes or gaps. We demonstrate that our approach improves the air-tightness of printed pneumatic actuators while reducing the need for fine-tuning printing parameters. Our approach presents a new option for robustly fabricating airtight, soft robotic actuators. 

Artificial intelligence‐augmented additive manufacturing (AI2AM) represents a transformative frontier in digital fabrication, where artificial intelligence (AI) is embedded not as a peripheral tool, but as a central framework driving intelligent, adaptive, and autonomous additive manufacturing (AM) systems. The objective of this Roadmap is to present a comprehensive vision of the state‐of‐the‐art developments in AI2AM while charting the future trajectory of this rapidly emerging field. As AM applications continue to expand across diverse sectors, conventional design and control strategies face growing limitations in scalability, quality assurance, and material complexity. AI uses tools like computer vision, generative design, and large language models to help solve problems in scalability, quality assurance, and material complexity, allowing for real‐time defect detection, digital twin integration, and closed‐loop process control. This roadmap brings together leading contributions from twenty internationally recognized research groups by uniting perspectives from materials science, computer science, robotics, and manufacturing. This work aims to create a cohesive framework for advancing AI2AM as a multidisciplinary science. The ultimate intent of this work is to establish a foundation for coordinated research and innovation in AI‐powered AM and to serve as a strategic entry point for future breakthroughs in autonomous and sustainable production. 

This paper presents a method of computing free motions of a planar assembly of rigid bodies connected by loose joints. Joints are modeled using local distance constraints, which are then linearized with respect to configuration space velocities, yielding a linear programming formulation that allows analysis of systems with thousands of rigid bodies. Potential applications include analysis of collections of modular robots, structural stability perturbation analysis, tolerance analysis for mechanical systems, and formation control of mobile robots. 

This paper presents a method of computing free motions of a planar assembly of rigid bodies connected by loose joints. Joints are modeled using local distance constraints, which are then linearized with respect to configuration space velocities, yielding a linear programming formulation that allows analysis of systems with thousands of rigid bodies. Potential applications include analysis of collections of modular robots, structural stability perturbation analysis, tolerance analysis for mechanical systems, and formation control of mobile robots.