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In this paper we describe ICARUS, an adaptive architecture for intelligent physical agents. We contrast the framework's assumptions with those of earlier architectures, taking examples from an in-city driving task to illustrate our points. Key differences include: primacy of perception and action over problem solving, separate memories for categories and skills, a hierarchical organization on both memories, strong correspondence between long-term and short-term structures, and cumulative learning of skill hierarchies. We support claims for ICARUS' generality by reporting our experience with driving and three other domains. In closing, we discuss limitations of the current architecture and propose extensions that would remedy them.