Executing Underspecified Actions in Real World Based on Online Projection

Abstract

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. In this paper, we present a mechanism for a robot that is acting in a real-world environment to think ahead of time with fast plan projection and, thereby, choose 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 place actions in a real world setting.