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Service Robots for Citizens of the Future

  • Carme Torras (a1)

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.

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2.For example, projects RoboEarth (http://roboearth.org/) and RoboHow (http://robohow.eu/).
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. 2029.
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. 13401353.
5.Billard, A., Calinon, S., Dillmann, R. and Schaal, S. (2008) Robot programming by demonstration. Handbook of Robotics, (Springer), ch. 59, pp. 13711394.
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. 3351.
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).
8.Peters, J. and Schaal, S. (2008) Reinforcement learning of motor skills with policy gradients. Neural Networks, 21(4), pp. 682697.
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.
10.Paraschos, A., Neumann, G., Daniel, C. and Peters, J. (2013) Probabilistic movement primitives. Advances in Neural Information Processing Systems (NIPS), (Cambridge), pp. 26162624.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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. 246258.
25.Simo-Serra, E., Torras, C. and Moreno-Noguer, F. (2015) DaLI: deformation and light invariant descriptor. International Journal of Computer Vision, to appear.
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. 5059.
28.Torras, C. (1995) Robot adaptivity. Robotics and Autonomous Systems, 15(1), pp. 1123.
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. 304324.
30.Ruiz de Angulo, V. and Torras, C. (1997) Self-calibration of a space robot. IEEE Trans. on Neural Networks, 8(4), pp. 951963.
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.
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. 12151230.
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. 151180.
34.Martínez, D., Alenyà, G. and Torras, C. (2015) Relational reinforcement learning with guided demonstrations. Artificial Intelligence Journal, to appear.
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.
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. 574591.
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. 161190.
39.Torras, C. (2014) Social robots: a meeting point between science and fiction. Metode Science Studies Journal – Annual Review, 5, pp. 110115.
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. 269273.
41.Torras, C. (2012) La mutación sentimental (The sentimental mutation). Editorial Milenio.
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.
43.Agostini, A., Torras, C. and Wörgötter, F. (2015) Efficient interactive decision-making framework for robotic applications. Artificial Intelligence Journal, to appear.

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