Automatic generation of error recovery knowledge through learned reactivity

Develops a new method for automatic generation of error recovery procedures in the assembly workcell. Current reactive systems have integrated planning and learning capabilities to allow them to augment their store of reactive behaviors whenever they are forced to plan. The cycle of evaluation and reaction used by these systems implicitly detects and corrects any errors made in prior behavior execution. By capitalizing on this analogy to error recovery, the authors are able to gain the benefits of a reactive system for use in error recovery while minimizing the perception and evaluation burden that is inherent in those approaches. The authors present a system that uses the new learning reactive paradigm to quickly correct known errors and autonomously learn to recover new errors without human intervention