Title
Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach.
Abstract
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.
Year
DOI
Venue
2019
10.1007/s10846-019-01072-4
Journal of Intelligent & Robotic Systems
Keywords
Field
DocType
Learning from demonstration, Transfer learning, Data augmentation, Convolutional neural networks, Task parametrized Gaussian mixture models
iCub,Convolutional neural network,Knowledge transfer,Learning from demonstration,Dirt,Control engineering,Robustness (computer science),Human–computer interaction,Artificial intelligence,Deep learning,Engineering,Robot
Journal
Volume
Issue
ISSN
98
1
0921-0296
Citations 
PageRank 
References 
1
0.36
24
Authors
7
Name
Order
Citations
PageRank
Jaeseok Kim140558.33
Nino Cauli2233.03
Vicente, P.3155.46
Bruno Damas4546.25
Alexandre Bernardino571078.77
Santos-Victor, J.61747169.53
Filippo Cavallo731158.89