Prototyping large, electronically integrated structures is challenging and often results in unwieldy wiring, weak mechanical properties, expensive iterations, or limited reusability. While many electronics prototyping kits exist for small-scale objects, relatively few methods exist to freely iterate large and sturdy structures with integrated electronics. To address this gap, we present the Voxel Invention Kit (VIK), which uses reconfigurable blocks that assemble into high-stiffness, lightweight structures with integrated electronics. We do this by creating cubic blocks composed of PCBs that carry electrical routing and components and can be (re)configured with simple tools into a variety of structures. To ensure structural stability without expertise, we created a tool to configure structures and simulate applied loads, which we validated with mechanical testing data. Using VIK, we produced devices reconfigured from a shared set of voxels: multiple iterations of a customizable AV lounge seat, a dance floor game, and a force-sensing bridge.

We present a robotic system for the assembly of 3D discrete lattice structures in which the robots are able to self-reproduce, such that the assembly system may scale its own parallelization. Robots and structures are made from a set of compatible building blocks, or voxels, which can be assembled and reassembled into more complex structures. Robotic modules are made by combining actuators with a functional voxel, which routes electrical power and signals. Robotic modules then assemble into reconfigurable robots via a reversible solder joint. The robot assembles higher performance structures using a set of construction voxels, which do not contain electrical features. This paper describes the design, development, and evaluation of this assembly system, including the robotic hardware, lattice material, and planning and controls methods. We demonstrate the system through a set of fundamental assembly tasks: the robot assembling another robot, and the two robots collaborating to assemble a small structure.

With recent advances in Generative AI, it is becoming easier to automatically manipulate 3D models. However, current methods tend to apply edits to models globally, which risks compromising the intended functionality of the 3D model when fabricated in the physical world. For example, modifying functional segments in 3D models, such as the base of a vase, could break the original functionality of the model, thus causing the vase to fall over. We introduce a method for automatically segmenting 3D models into functional and aesthetic elements. This method allows users to selectively modify aesthetic segments of 3D models, without affecting the functional segments. To develop this method we first create a taxonomy of functionality in 3D models by qualitatively analyzing 1000 models sourced from a popular 3D printing repository, Thingiverse. With this taxonomy, we develop a semi-automatic classification method to decompose 3D models into functional and aesthetic elements. We propose a system called Style2Fab that allows users to selectively stylize 3D models without compromising their functionality. We evaluate the effectiveness of our classification method compared to human-annotated data, and demonstrate the utility of Style2Fab with a user study to show that functionality-aware segmentation helps preserve model functionality.

In this video, we consider recognition and reconfiguration of lattice-based cellular structures by very simple robots with only basic functionality. The underlying motivation is the construction and modification of space facilities of enormous dimensions, where the combination of new materials with extremely simple robots promises structures of previously unthinkable size and flexibility. We present algorithmic methods that are able to detect and reconfigure arbitrary polyominoes, based on finite-state robots, while also preserving connectivity of a structure during reconfiguration. Specific results include methods for determining a bounding box, scaling a given arrangement, and adapting more general algorithms for transforming polyominoes.

We consider recognition and reconfiguration of lattice-based cellular structures by very simple robots with only basic functionality. The underlying motivation is the construction and modification of space facilities of enormous dimensions, where the combination of new materials with extremely simple robots promises structures of previously unthinkable size and flexibility; this is also closely related to the newly emerging field of programmable matter. Aiming for large-scale scalability, both in terms of the number of the cellular components of a structure, as well as the number of robots that are being deployed for construction requires simple yet robust robots and mechanisms, while also dealing with various basic constraints, such as connectivity of a structure during reconfiguration. To this end, we propose an approach that combines ultra-light, cellular building materials with extremely simple robots. We develop basic algorithmic methods that are able to detect and reconfigure arbitrary cellular structures, based on robots that have only constant-sized memory. As a proof of concept, we demonstrate the feasibility of this approach for specific cellular materials and robots that have been developed at NASA.