Classical artificial intelligence planning is not sufficiently robust in uncertain and dynamic environments. Reactive approaches are robust in some environments-namely those for which they have been programmed. An approach that integrates a priori planning with reaction to increase robustness is presented. As motivation, a practical robot problem, the kitting robot, is presented. Solving this problem demands a system that can make timely and robust actions in an uncertain environment. A solution to the kitting robot problem in which planning is cast as adaptation of a reactive system to suit changes in the goals or environment is outlined. The reactive system (the reactor) is based on a formal model for representing flexible robot plans, the RS model. Thus, it was possible to formalize the mechanisms by which the planner improves the behavior of the reactor. This system was implemented to control a Puma-560 robot equipped with visual sensing.>

Robot applications, represented as plans, are used to outline a viewpoint that robustness needs to be emphasized in two areas: in the plan representation and in the underlying system software. Robot applications are inherently distributed, since the hardware usually comprises a set of independent actuators and sensors, with the robot programs acting as links between them. A special model of distributed computation, the RS (Robot Schemas) model, has been designed to handle the issues of robot plan representation, and an overview of the model is presented. An initial implementation of the model with minimal execution support demonstrated that the domain-dependent aspect of robustness on its own was not sufficient for robust behavior. Consequently, the OS has been augmented with real-time scheduling, and monitoring facilities.>