  Nowadays, autonomous service robots are becoming an important topic in
robotic research. Differently from typical industrial scenarios, with highly
controlled environments, service robots must show an additional robustness to
task perturbations and changes in the characteristics of their sensory
feedback. In this paper, a robot is taught to perform two different cleaning
tasks over a table, using a learning from demonstration paradigm. However,
differently from other approaches, a convolutional neural network is used to
generalize the demonstrations to different, not yet seen dirt or stain patterns
on the same table using only visual feedback, and to perform cleaning movements
accordingly. Robustness to robot posture and illumination changes is achieved
using data augmentation techniques and camera images transformation. This
robustness allows the transfer of knowledge regarding execution of cleaning
tasks between heterogeneous robots operating in different environmental
settings. To demonstrate the viability of the proposed approach, a network
trained in Lisbon to perform cleaning tasks, using the iCub robot, is
successfully employed by the DoRo robot in Peccioli, Italy.
