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Service Robots for Citizens of the Future

Published online by Cambridge University Press:  09 February 2016

Carme Torras
Carme Torras*
Affiliation:
Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Barcelona, Spain. E-mail: torras@iri.upc.edu
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Abstract

Robots are no longer confined to factories; they are progressively spreading to urban, social and assistive domains. In order to become handy co-workers and helpful assistants, they must be endowed with quite different abilities from their industrial ancestors. Research on service robots aims to make them intrinsically safe to people, easy to teach by non-experts, able to manipulate not only rigid but also deformable objects, and highly adaptable to non-predefined and dynamic environments. Robots worldwide will share object and environmental models, their acquired knowledge and experiences through global databases and, together with the internet of things, will strongly change the citizens’ way of life in so-called smart cities. This raises a number of social and ethical issues that are now being debated not only within the Robotics community but by society at large.

Information

Type
Erasmus Lecture 2014
Information
European Review , Volume 24 , Issue 1 , February 2016 , pp. 17 - 30
Copyright
© Academia Europaea 2016 

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References

1.http://sparc-robotics.eu/.Google Scholar
2.For example, projects RoboEarth (http://roboearth.org/) and RoboHow (http://robohow.eu/).Google Scholar
3.Kemp, C. C., Edsinger, A. and Torres-Jara, E. (2007) Challenges for robot manipulation in human environments. IEEE Robotics and Automation Magazine, 14(1), pp. 20–29.CrossRefGoogle Scholar
4.Smith, C., Karayiannidis, Y., Nalpantidis, L., Gratal, X., Qi, P., Dimarogonas, D. V. and Kragic, D. (2012) Dual arm manipulation – a survey. Robotics and Autonomous systems, 60(10), pp. 1340–1353.CrossRefGoogle Scholar
5.Billard, A., Calinon, S., Dillmann, R. and Schaal, S. (2008) Robot programming by demonstration. Handbook of Robotics, (Springer), ch. 59, pp. 1371–1394.CrossRefGoogle Scholar
6.Rozo, L., Jiménez, P. and Torras, C. (2013) A robot learning from demonstration framework to perform force-based manipulation tasks. Intelligent Service Robotics, 6(1), pp. 33–51.CrossRefGoogle Scholar
7.Sutton, R. S. and Barto, A. G. (1998) Reinforcement Learning: An Introduction (Cambridge: MIT), (second edition at https://www.dropbox.com/s/f4tnuhipchpkgoj/book2012.pdf).Google Scholar
8.Peters, J. and Schaal, S. (2008) Reinforcement learning of motor skills with policy gradients. Neural Networks, 21(4), pp. 682–697.Google ScholarPubMed
9.Colomé, A. and Torras, C. (2014) Dimensionality reduction and motion coordination in learning trajectories with dynamic movement primitives. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, pp. 1414–1420.CrossRefGoogle Scholar
10.Paraschos, A., Neumann, G., Daniel, C. and Peters, J. (2013) Probabilistic movement primitives. Advances in Neural Information Processing Systems (NIPS), (Cambridge), pp. 2616–2624.Google Scholar
11.Colomé, A., Neumann, G., Peters, J. and Torras, C. (2014) Dimensionality reduction for probabilistic movement primitives. IEEE-RAS International Conference on Humanoid Robots, Madrid, Spain, pp. 794–800.CrossRefGoogle Scholar
12.Rozo, L., Calinon, S., Caldwell, D., Jiménez, P. and Torras, C. (2013) Learning collaborative impedance-based robot behaviors. 27th International Conference of the Association for the Advancement of Artificial Intelligence (AAAI-13), Bellevue, Washington, pp. 1422–1428.CrossRefGoogle Scholar
13.http://www.saphari.eu/.Google Scholar
14.Zanchettin, A. M., Ceriani, N. M., Rocco, P., Ding, H. and Matthias, B. (2015) Safety in human-robot collaborative manufacturing environments: metrics and control. IEEE Transactions on Automation Science and Engineering, to appear.CrossRefGoogle Scholar
15.De Luca, A. and Flacco, F. (2012) Integrated control for pHRI: Collision avoidance, detection, reaction and collaboration. Fourth IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), Rome, Italy, pp. 288–295.Google Scholar
16.Colomé, A., Pardo, D., Alenyà, G. and Torras, C. (2013) External force estimation during compliant robot manipulation. IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, pp. 3535–3540.CrossRefGoogle Scholar
17.Colomé, A., Planells, A. and Torras, C. (2015) A friction-model-based framework for reinforcement learning of robotic tasks in non-rigid environments. IEEE International Conference on Robotics and Automation (ICRA), Seattle, pp. 5649–5654.CrossRefGoogle Scholar
18.http://www.iri.upc.edu/groups/perception/#ScarfTask.Google Scholar
19.Cusumano-Towner, M., Singh, A., Miller, S., O’Brien, J. F. and Abbeel, P. (2011) Bringing clothing into desired configurations with limited perception. IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, pp. 3893–3900.CrossRefGoogle Scholar
20.Doumanoglou, A., Kargakos, A., Kim, T-K. and Malassiotis, S. (2014) Autonomous active recognition and unfolding of clothes using random decision forests and probabilistic planning. IEEE International Conference. on Robotics and Automation (ICRA), Hong Kong, pp. 987–993.CrossRefGoogle Scholar
21.http://www.iri.upc.edu/groups/perception/#clothingDataset.Google Scholar
22.Ramisa, A., Alenyà, G., Moreno-Noguer, F. and Torras, C. (2012) Using depth and appearance features for informed robot grasping of highly wrinkled clothes. IEEE International Conference on Robotics and Automation (ICRA), St. Paul, Minnesota, pp. 1703–1708.CrossRefGoogle Scholar
23.Ramisa, A., Alenyà, G., Moreno-Noguer, F. and Torras, C. (2013) FINDDD: a fast 3D descriptor to characterize textiles for robot manipulation. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, pp. 824–830.CrossRefGoogle Scholar
24.Ramisa, A., Alenyà, G., Moreno-Noguer, F. and Torras, C. (2014) Learning RGB-D descriptors of garment parts for informed robot grasping. Engineering Applications of Artificial Intelligence, 35, pp. 246–258.CrossRefGoogle Scholar
25.Simo-Serra, E., Torras, C. and Moreno-Noguer, F. (2015) DaLI: deformation and light invariant descriptor. International Journal of Computer Vision, to appear.CrossRefGoogle Scholar
26.http://www.garnics.eu/.Google Scholar
27.Alenyà, G., Dellen, B., Foix, S. and Torras, C. (2013) Robotized plant probing: leaf segmentation utilizing time-of-flight data. IEEE Robotics and Automation Magazine, 20(3), pp. 50–59.CrossRefGoogle Scholar
28.Torras, C. (1995) Robot adaptivity. Robotics and Autonomous Systems, 15(1), pp. 11–23.CrossRefGoogle Scholar
29.Hoffmann, M., Gravato, H., Hernandez, A., Sumioka, H., Lungarella, M. and Pfeifer, R. (2010) Body schema in robotics: a review. IEEE Transactions on Autonomous Mental Development, 2(4), pp. 304–324.CrossRefGoogle Scholar
30.Ruiz de Angulo, V. and Torras, C. (1997) Self-calibration of a space robot. IEEE Trans. on Neural Networks, 8(4), pp. 951–963.CrossRefGoogle Scholar
31.Ulbrich, S., Ruiz de Angulo, V., Asfour, T., Torras, C. and Dillman, R. (2009) Rapid learning of humanoid body schemas with kinematic Bezier maps. 9th IEEE International Conference on Humanoid Robots, Paris, pp. 431–438.CrossRefGoogle Scholar
32.Ulbrich, S., Ruiz de Angulo, V., Asfour, T., Torras, C. and Dillman, R. (2012) Kinematic Bézier maps. IEEE Transactions on Systems, Man and Cybernetics: Part B, 42(4), pp. 1215–1230.CrossRefGoogle ScholarPubMed
33.Vernon, D., Metta, G. and Sandini, G. (2007) A survey of artificial cognitive systems: Implications for the autonomous development of mental capabilities in computational agents. IEEE Transactions on Evolutionary Computation, 11(2), pp. 151–180.CrossRefGoogle Scholar
34.Martínez, D., Alenyà, G. and Torras, C. (2015) Relational reinforcement learning with guided demonstrations. Artificial Intelligence Journal, to appear.Google Scholar
35.Martínez, D., Alenyà, G. and Torras, C. (2015) Safe robot execution in model-based reinforcement learning. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, to appear.CrossRefGoogle Scholar
36.Ballesté, F. and Torras, C. (2013) Effects of human-machine integration on the construction of identity. In: R. Luppicini, (ed.), Handbook of Research on Technoself: Identity in a Technological Society (Hershey, USA: IGI Global), ch. 30, pp. 574–591.CrossRefGoogle Scholar
37.http://www.worldpolicy.org/journal/fall2011/innovation-starvation.Google Scholar
38.Sharkey, N. and Sharkey, A. (2010) The crying shame of robot nannies. Interaction Studies: Social Behaviour and Communication in Biological and Artificial Systems, 11(2), pp. 161–190.CrossRefGoogle Scholar
39.Torras, C. (2014) Social robots: a meeting point between science and fiction. Metode Science Studies Journal – Annual Review, 5, pp. 110–115.Google Scholar
40.Torras, C. (2010) Robbie, the pioneer robot nanny: science fiction helps develop ethical social opinion. Interaction Studies: Social Behaviour and Communication in Biological and Artificial Systems, 11(2), pp. 269–273.CrossRefGoogle Scholar
41.Torras, C. (2012) La mutación sentimental (The sentimental mutation). Editorial Milenio.Google Scholar
42.Agostini, A., Torras, C. and Wörgötter, F. (2011) Integrating task planning and interactive learning for robots to work in human environments. International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, pp. 2386–2391.Google Scholar
43.Agostini, A., Torras, C. and Wörgötter, F. (2015) Efficient interactive decision-making framework for robotic applications. Artificial Intelligence Journal, to appear.Google Scholar
44.http://www.stopkillerrobots.org/2015/07/aicall/.Google Scholar