This project is about finding tractable models for unstructured environments to help robots reason about the long-term outcomes of different modification strategies. By on representations that are designed to capture uncertainty, we can build robotic systems that can work with a variety of challenging building materials, such as deformable bags and poly-urethan foam, yet have a high degree of certainty that they will succeed. The trick is that we are trading off predictability of the exact final shape of a structure for better chances of success. One or more robots continually re-scan and evaluate their environment and then make incremental modifications toward a common goal, e.g., building a large ramp or level surface. We focus on mid-level abstractions that allow us both to formulate plans during execution and serve as a design tool. For example, we can answer questions like: Can a particular robot platform use material X to build structure Y? What combinations of materials and robots make sense if I want to build a specific structure within a certain tolerance?