Robots performing general purpose plans must deal with a wide variety of contexts. Situations they encounter might differ only in subtle, but important details in context and parameterization that have a massive impact on an action's outcome. To avoid the effort of encoding all possible combinations of subtleties into plans, we present a prediction framework that gives robot agents an intuition of their actions' effects and for choosing parameter values that have proven to be useful before. We let a robot form these predictions and expectations from episodic memories collected during earlier plan executions, improving its own behavior with every new situation encountered. We evaluate and explain our approach using experiments performed on a PR2 robot performing complex mobile manipulation activities in a kitchen environment.
