Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach

Abstract

In this paper, a robot is taught to perform two different cleaning tasks over a table, using a learning from demonstration paradigm. Robustness to robot posture and illu- mination changes is achieved using data augmentation techniques and camera images transformation. This robustness allows the transfer of knowledge regarding execution of cleaning tasks be- tween heterogeneous robots operating in different environmental settings. To demonstrate the viability of the proposed approach, a CNN network trained in Lisbon to perform cleaning tasks, using the iCub robot,is successfully employed by the DoRo robot in Peccioli, Italy.