URoboSim -- An Episodic Simulation Framework for Prospective Reasoning in Robotic Agents

TL;DR

URoboSim is a novel episodic simulation framework enabling robots to mentally simulate actions and their physical outcomes, enhancing prospective reasoning and decision-making capabilities in robotic agents.

Contribution

This paper introduces URoboSim, a new simulation framework that allows robots to perform mental simulations for improved task planning and learning.

Findings

URoboSim effectively generates mental simulations for robotic tasks.

The framework provides data for machine learning applications.

URoboSim can serve as a belief state for real robots.

Abstract

Anticipating what might happen as a result of an action is an essential ability humans have in order to perform tasks effectively. On the other hand, robots capabilities in this regard are quite lacking. While machine learning is used to increase the ability of prospection it is still limiting for novel situations. A possibility to improve the prospection ability of robots is through simulation of imagined motions and the physical results of these actions. Therefore, we present URoboSim, a robot simulator that allows robots to perform tasks as mental simulation before performing this task in reality. We show the capabilities of URoboSim in form of mental simulations, generating data for machine learning and the usage as belief state for a real robot.