Towards Plan Transformations for Real-World Pick and Place Tasks
  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.
