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

## TL;DR

This paper explores plan transformations to adapt and optimize robotic manipulation plans in real-world pick and place tasks, enhancing performance through autonomous runtime modifications.

## Contribution

It introduces a framework for transforming complex robot plans at runtime to improve execution efficiency in real-world environments.

## Key findings

- Transformations successfully adapted plans for specific tasks.
- Enhanced execution performance demonstrated in real-world experiments.
- Framework applicable to complex control structures in robotic plans.

## Abstract

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.