URoboSim -- An Episodic Simulation Framework for Prospective Reasoning in Robotic Agents
This work addresses the problem of limited prospective reasoning in robotic agents, particularly in novel situations, by providing a simulation framework for improved task execution.
This paper introduces URoboSim, a robotic simulator designed to enable robots to perform mental simulations of tasks before executing them in the real world. The framework demonstrates its utility in mental simulations, generating data for machine learning, and serving as a belief state for a physical robot.
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.