Robots Avoid Potential Failures through Experience-based Probabilistic Planning

Abstract

Robots should avoid potential failure situations to safely execute their actions and to improve their performances. For this purpose, they need to build and use their experience online. We propose online learning-guided planning methods to address this problem. Our method includes an experiential learning process using Inductive Logic Programming (ILP) and a probabilistic planning framework that uses the experience gained by learning for improving task execution performance. We analyze our solution on a case study with an autonomous mobile robot in a multi-object manipulation domain where the objective is maximizing the number of collected objects while avoiding potential failures using experience. Our results indicate that the robot using our adaptive planning strategy ensures safety in task execution and reduces the number of potential failures.