Becoming action-aware through reasoning about logged plan execution traces

Robots that know what they are doing can solve their tasks more reliably, flexibly, and efficiently. They can even explain what they were doing, how and why. In this paper we describe a system that not only is capable of executing flexible and reliable plans on a robotic platform but can also explain control decisions and the reason for specific actions, diagnose the cause of failures and answer queries about the robot's beliefs. For instance, when queried why it opened the cupboard door, the robot might answer that it did so because it believed Michael's cup to be in there. This type of reasoning is not only helpful for debugging but also provides the mechanisms for complex monitoring and failure handling that is not based on local failures and exception handling but on the expressive formulation of error patterns in first order logics. Our system is based on semantic annotations of plans, a fast logging mechanism and the computation of predicates in a first-order representation based on the execution trace.