Planning surface cleaning tasks by learning uncertain drag actions outcomes

Abstract

A method to perform cleaning tasks is presented where a robot manipulator autonomously grasps a textile and uses different dragging actions to clean a surface. Actions are imprecise, and probabilistic planning is used to select the best sequence of actions. The characterization of such actions is complex because the initial autonomous grasp of the textile introduces differences in the initial conditions that change the efficacy of the robot cleaning actions. We demonstrate that the action outcome probabilities can be learned very fast while the task is being executed, so as to progressively improve robot performance. The learner adds only a little overhead to the system compared to the improvements obtained. Experiments with a real robot show that the most effective plan varies depending on the initial grasp, and that plans become better after only a few learning iterations.