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Plan execution on real robots in realistic environments is underdeterminedand often leads to failures. The choice of action parameterization is crucialfor task success. By thinking ahead of time with the fast plan projectionmechanism proposed in this paper, a general plan can be specialized towards theenvironment and task at hand by choosing action parameterizations that arepredicted to lead to successful execution. For finding causal relationshipsbetween action parameterizations and task success, we provide the robot withmeans for plan introspection and propose a systematic and hierarchical planstructure to support that. We evaluate our approach by showing how a PR2 robot,when equipped with the proposed system, is able to choose actionparameterizations that increase task execution success rates and overallperformance of fetch and deliver actions in a real world setting.

Specializing Underdetermined Action Descriptions Through Plan Projection