Towards Plan Transformations for Real-World Pick and Place Tasks
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In this paper, we investigate the possibility of applying plan transformations to general manipulation plans in order to specialize them to the specific situation at hand. We present a framework for optimizing execution and achieving higher performance by autonomously transforming robot's behavior at runtime. We show that plans employed by robotic agents in real-world environments can be transformed, despite their control structures being very complex due to the specifics of acting in the real world. The evaluation is carried out on a plan of a PR2 robot performing pick and place tasks, to which we apply three example transformations, as well as on a large amount of experiments in a fast plan projection environment.
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