Specializing Underdetermined Action Descriptions Through Plan Projection
This addresses plan execution failures for robots in realistic environments, though it is incremental as it builds on existing plan projection and introspection methods.
The paper tackles the problem of underdetermined action descriptions in robot plan execution by using fast plan projection to specialize general plans for specific environments and tasks, resulting in increased success rates and performance for fetch and deliver actions on a PR2 robot.
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.