Towards Plan Transformations for Real-World Pick and Place Tasks
This addresses the challenge of robotic adaptability in dynamic environments, though it appears incremental as it builds on existing plan transformation concepts.
The paper tackles the problem of adapting general manipulation plans to specific real-world situations by developing a framework for runtime plan transformations, demonstrating that complex robot control structures can be transformed to optimize execution in 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.