We are implementing ADAPT, a cognitive architecture for a Pioneer mobile robot, to give the robot the full range of cognitive abilities including perception, use of natural language, learning and the ability to solve complex problems. Our perspective is that an architecture based on a unified theory of robot cognition has the best chance of attaining human-level performance. Existing work in cognitive modeling has accomplished much in the construction of such unified cognitive architectures in areas other than robotics; however, there are major respects in which these architectures are inadequate for robot cognition. This paper examines two major inadequacies of current cognitive architectures for robotics: the absence of support for true concurrency and for active perception. ADAPT models the world as a network of concurrent processes, and models perception as problem solving. ADAPT integrates three theories: the theory of cognition embodied in the Soar system, the RS formal model of concurrent sensorimotor activity and an algebraic theory of decomposition and reformulation. These three component theories have been implemented and tested separately and their integration is currently underway. This paper describes these components and the plan for their integration.