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Foams, essential for applications from car seats to thermal insulation, are limited by traditional manufacturing techniques that struggle to produce graded stiffness, a key feature for enhanced functionality. Here, we introduce a novel slicing algorithm for producing heterogeneous foams through viscous thread printing (VTP). Our slicer generates a single, global toolpath for the entire foam volume while modulating the viscous thread’s self-interactions along this path to program stiffness. The slicer integrates multiple meshes into a unified print space and interpolates the print speed and height based on specified mesh parameters to program the desired stiffness variations. Using both qualitative samples and quantitative compression tests, we demonstrate that our slicer can (1) generate foam stiffnesses spanning an order of magnitude, (2) achieve millimeter precision in stiffness control, and (3) continuously vary stiffness between regions of constant stiffness using arbitrary functional forms. 

This work proposes a novel generative design tool for passive grippers---robot end effectors that have no additional actuation and instead leverage the existing degrees of freedom in a robotic arm to perform grasping tasks. Passive grippers are used because they offer interesting trade-offs between cost and capabilities. However, existing designs are limited in the types of shapes that can be grasped. This work proposes to use rapid-manufacturing and design optimization to expand the space of shapes that can be passively grasped. Our novel generative design algorithm takes in an object and its positioning with respect to a robotic arm and generates a 3D printable passive gripper that can stably pick the object up. To achieve this, we address the key challenge of jointly optimizing the shape and the insert trajectory to ensure a passively stable grasp. We evaluate our method on a testing suite of 22 objects (23 experiments), all of which were evaluated with physical experiments to bridge the virtual-to-real gap. Code and data are at https://homes.cs.washington.edu/~milink/passive-gripper/