We present a hierarchical framework to solve robot planning as an input control problem. At the lowest level are temporary closed control loops, ("tasks"), each representing a behaviour, contingent on a specific sensory input and therefore temporary. At the highest level, a supervising "Configurator" directs task creation and termination. Here resides "core" knowledge as a physics engine, where sequences of tasks can be simulated. The Configurator encodes and interprets simulation results,based on which it can choose a sequence of tasks as a plan. We implement this framework on a real robot and test it in an overtaking scenario as proof-of-concept.
@article{arxiv.2411.11510,
title = {Closed-loop multi-step planning with innate physics knowledge},
author = {Giulia Lafratta and Bernd Porr and Christopher Chandler and Alice Miller},
journal= {arXiv preprint arXiv:2411.11510},
year = {2024}
}