Abstract
The Robotic Ecology vision shares many similarities with the one pursued by the IoT community: The ideal aim on both fronts is that arbitrary combinations of devices should be able to be deployed in unstructured environments, such as those exemplified in a typical household, and there efficiently cooperate to the achievement of complex tasks. While this has the potential to deliver a range of modular and disruptive applications, a pressing and open research question is how to reduce the amount of pre-programming required for their deployment in real world applications. In order to inspire similar advancements within the IoT community, this extended abstract discusses how this goal has been addressed by pioneering the concept of a self-learning robotic ecology within the EU project RUBICON (Robotic UBIquitous Cognitive Network); how such an approach relates to the concept of Affordances at the basis of Gibsons’ theory of ecological psychology; and how it can be used to drive the gradual adaptation of a robotic ecology to changing contexts and evolving requirements.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Saffiotti, A., Broxvall, M.: Affordances in an ecology of physically embedded intelligent systems. In: Rome, E., Hertzberg, J., Dorffner, G. (eds.) Towards Affordance-Based Robot Control. LNCS (LNAI), vol. 4760, pp. 106–121. Springer, Heidelberg (2008)
Greeno, J.G.: Gibson’s affordances. Psychol. Rev. 1012, 336–342 (1994)
Gibson, J.J.: The Theory of Affordances. Lawrence Erlbaum (1977)
Robotic ubiquitous cognitive network (rubicon) website (2014). http://www.fp7rubicon.eu/
Horton, T.E., Chakraborty, A., Amant, R.S.: Affordances for robots: A brief survey. Avant 3(2), 70–84 (2012)
Bacciu, D., Gallicchio, C., Micheli, A., Di Rocco, M., Saffiotti, A.: Learning context-aware mobile robot navigation in home environments. In: The 5th International Conference on Information, Intelligence, Systems and Applications, IISA 2014, pp. 57–62, July 2014
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Dragone, M. (2015). A Cognitive Approach to Affordance Learning in Robotic Ecologies. In: Giaffreda, R., et al. Internet of Things. User-Centric IoT. IoT360 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-319-19656-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-19656-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19655-8
Online ISBN: 978-3-319-19656-5
eBook Packages: Computer ScienceComputer Science (R0)