  Plan execution on real robots in realistic environments is underdetermined
and often leads to failures. The choice of action parameterization is crucial
for task success. By thinking ahead of time with the fast plan projection
mechanism proposed in this paper, a general plan can be specialized towards the
environment and task at hand by choosing action parameterizations that are
predicted to lead to successful execution. For finding causal relationships
between action parameterizations and task success, we provide the robot with
means for plan introspection and propose a systematic and hierarchical plan
structure to support that. We evaluate our approach by showing how a PR2 robot,
when equipped with the proposed system, is able to choose action
parameterizations that increase task execution success rates and overall
performance of fetch and deliver actions in a real world setting.
