Abstract Motivated by the need for tools to aid in the design of effective robots, we examine how to determine the role that particular sensing and actuator resources play in enabling a robot to achieve useful ends. Rather than merely asking "will this sensor suffice?" we classify general modifications to the set of sensors and actuators based on the feasibility of accomplishing given tasks using these sets. The goal is to probe the boundary between modifications that are destructive on a given planning problem, and modifications that are not. Since this boundary itself can be impractically large, classic search methods are of no avail to summarize discriminatory features on this boundary. Instead, we propose a decision tree learning method to efficiently construct a compact implicit representation of the boundary. The idea is to allow the designer to use prior knowledge to constrain the search, then use the tool to probe the boundary subject to those constraints, gaining insight into the information necessary for a robot to ensure task achievement. Ultimately we envision a interactive process where additional constraints are repeatedly included as new light is shed. We aim to pave the way for interactive tools that help the roboticist navigate the complexities of the design space. We describe an implementation of this approach along with experimental results that show that the method can construct decision trees with explanatory value. Our experiments suggest that some domain knowledge (specifically picking features that emphasize monotonicity) substantially improves running-time with only negligible reduction in accuracy.