Recently there has been a growing interest in using robots in activities that are dangerous or cost prohibitive for humans to do. Such activities include military uses and space exploration. While robotic hardware is often capable of being used in these types of situations, the ability of human operators to control robots in an effective manner is often limited. This deficiency is often related to the control interface of the robot and the level of autonomy that control system affords the human operator. This thesis describes a robot control system, called the safe/unsafe system, which gives a human operator the ability to quickly define how the system can cause the robot to automatically perform obstacle avoidance. This definition system uses interactive machine learning to ensure that the obstacle avoidance is both easy for a human operator to use and can perform well in different environments. Initial, real world tests show that system is effective at automatic obstacle avoidance.