In this paper we define a complexity ratio that measures the degree to which a robot is self-replicating based on the number and complexity of subsystems that it can assemble to form a functional replica. We also quantify how structured the environment must be in order for a robot to function. This calculation uses Sanderson's concept of parts entropy. Together, the complexity measure of the robot and environmental entropy provide quantitative benchmarks to assess the state of the art in the subfield of self-replicating robotic systems, and provide goals for the design of future systems. We demonstrate these principles with three prototype systems that show different degrees of robotic self-replication. The first robot is controlled by a microprocessor and consists of five subsystems. The second has no microprocessor and is implemented as a finite-state machine consisting of discrete logic chips that are distributed over five subsystems. The third design consists of six subsystems and is able to handle greater environmental entropy. These systems demonstrate the desired progression towards self-replicating robots consisting of greater numbers of subsystems, each of lower complexity, and which are able to function in environments with increasing levels of disorder.