  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.
