Presentado al 23rd ICAPS (Workshop on PlanRob) 2013 celebrado en Roma del 10 al 14 de junio. ; 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. ; This work was supported by the Spanish Ministry of Science and Innovation under project PAU+ DPI2011-27510, by EU Project IntellAct FP7-ICT2009-6-269959 and by the Catalan Research Commission through SGR-00155. ; Peer Reviewed
Planning surface cleaning tasks by learning uncertain drag actions outcomes
2013-01-01
Conference paper
Electronic Resource
English
DDC: | 629 |
Planning surface cleaning tasks by learning uncertain drag actions outcomes
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