Learning to diagnose failures of assembly tasks

An architecture for execution supervision of Robotic Assembly Tasks is presented. This architecture provides, at different levels of abstraction, functions for dispatching actions, monitoring their execution, and diagnosing and recovering from failures. Modeling execution failures through taxonomies and causal networks plays a central role in diagnosis and recovery. A discussion on the knowledge acquisition process, through the use of machine learning techniques, is made. Preliminary results in this area are presented and planned extensions discussed.