Metamorphic robots, such as RoFI, are composed of many modules that can connect and form various shapes of the overall robot. To fully utilize the versatility of such metamorphic systems, we need an efficient way of computing of reconfiguration plan. One of the traditional approaches for computing such plans is state-space exploration. Such approaches are simple and can yield optimal reconfiguration plans. However, naive implementations of this approach do not scale well as the problem has enormous state space. Limiting degrees of freedom, restricting movement granularity, and leveraging module symmetries are ways to tackle large state spaces. The student should develop and implement an efficient representation of such restricted RoFI configurations and experimentally evaluate the improvements (if any) they provide. The student should also choose suitable state-space exploration heuristics (e.g., A*, RRT, iterative deepening) and explore if and how they can be deployed using the representation.