Abstract This paper aims to improve the practical scalability of automated tools to assist in designing robots. Such problems rapidly become intractable because the underlying design space is immense. We consider a specific type of design tool addressed in prior work, which constructs a representation of the destructiveness boundary in the space of robot designs. This prior work showed that a legible representation, specifically a decision tree, of this boundary can illuminate which elements of a sensing or actuation system are most important for enabling the robot to complete its task. In that context, the robot's interaction with the world is represented as procrustean graph, and the space of robot designs is represented by the space of label maps that rewrite the labels on that graph. In this paper, we expand upon those results by showing how domain knowledge can enable such tools to find solutions to more complex problems within a reasonable time frame. Specifically, we propose three different scenarios, expressed as constraints on the p-graph and on the label maps, under which the learning algorithm to identify the destructiveness boundary can converge quickly to high accuracy results for problems at larger scales than the prior, general-purpose algorithm. The conditions for each of these scenarios are easily verifiable and the set of problems that fall under each is rich enough to encompass several interesting problems. Experimental results demonstrate the effectiveness of the proposed methods.