Simulation tools are only as useful as the accuracy of the data and results that they produce. Particularly in robotics, when using machine learning techniques, there exists a real-world simulation gap. This means that data or learning schema produced in simulation, won't necessarily account for real-world nuances. In terra-mechanics (and therefore machine-soil interaction), this problem is only exacerbated by almost random terrain compounds (soils are a culmination of all the materials laden within it), and the terrains complex self-interactions. Even miniscule changes in mixture percentages (not to mention other environmental effects such as humidity) could have a large effect on the outcome of any particular attempt to manipulate the earth. However, within the field of terra-mechanics there is a lack of rigorous datasets that are able to provide well needed foundations to help guide the simulation tools. Utilising our industry standard 6-axis robotic manipulator and a custom-built earth-box, we will produce high quality datasets describing precise force measurements and the resulting terrain deformation. These tests will be repeated for numerous different shapes/tools/form factors. For example, most quadrupedal robots have simple ball feet. With this shape as the end effector, following a fully-fleshed out experimental process which considers all possible movements for a wide range of forces. During/following the gathering of these datasets, the project plans to build an autonomous zen-garden robot system (ZenBot) that is able to interact with the material and draw provided patterns. This would be a closed-loop system, where the current garden is compared against the desired garden. This is a challenge that encompasses not only the prior-mentioned problem but also requires skills ranging from terrain mapping, to control algorithm design. The data gathered would have many potential use cases. One of particular interest to me is utilising the knowledge of the terrain to inform locomotion policies for quadrupedal robotics. Knowing the environment could be used to understand how particular movements not only deform the terrain, but propel the robot in a given direction. This could enable a new wave of intelligent robotics, that not only understands where to move, but how to move most efficiently.