A generic architecture for evolutive supervision of robotized assembly tasks, in a context of integrated manufacturing systems, is presented. This architecture provides, at different levels of abstraction, functions for dispatching actions, monitoring their execution, and diagnosing and recovering from failures. The problem of integration of legacy systems is discussed and an implementation approach described. Modeling execution failures through taxonomies and causal relations plays a central role in diagnosis and recovery. Through the use of machine learning techniques, the supervision architecture will be given capabilities for improving its performance over time. Particular attention is given to the inductive generation of structured classification knowledge for diagnosis. Methodologies used, performed experiments, and obtained results are described in detail.
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