2018/12/19 by Gayane Kazhoyan, Kazhoyan, Gayane, Arthur Niedźwiecki +4
Computer Science · Engineering · #AI-based Problem Solving and Planning #FOS: Computer and information sciences #Robot Manipulation and Learning #Robotic Path Planning Algorithms #Robotics (cs.RO) #cs.RO
paper · pdf · doi:10.48550/arxiv.1812.08226
arxiv created 2018/12/19 · openalex publication_date 2018/12/19 · arxiv updated 2018/12/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.