Extreme Sensitive Robotic - A Context-Aware Ubiquitous Learning

Nicolas Verstaevel, Christine Régis, Valérian Guivarch, Marie-Pierre Gleizes, Fabrice Robert


Our work focuses on Extreme Sensitive Robotic that is on multi-robot applications that are in strong interaction with humans and their integration in a highly connected world. Because human-robots interactions have to be as natural as possible, we propose an approach where robots Learn from Demonstrations, memorize contexts of learning and self-organize their parts to adapt themselves to new contexts. To deal with Extreme Sensitive Robotic, we propose to use both an Adaptive Multi-Agent System (AMAS) approach and a Context-Learning pattern in order to build a multi-agent system ALEX (Adaptive Learner by Experiments) for contextual learning from demonstrations.


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Paper Citation

in Harvard Style

Verstaevel N., Régis C., Guivarch V., Gleizes M. and Robert F. (2015). Extreme Sensitive Robotic - A Context-Aware Ubiquitous Learning . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-073-4, pages 242-248. DOI: 10.5220/0005282002420248

in Bibtex Style

author={Nicolas Verstaevel and Christine Régis and Valérian Guivarch and Marie-Pierre Gleizes and Fabrice Robert},
title={Extreme Sensitive Robotic - A Context-Aware Ubiquitous Learning},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},

in EndNote Style

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Extreme Sensitive Robotic - A Context-Aware Ubiquitous Learning
SN - 978-989-758-073-4
AU - Verstaevel N.
AU - Régis C.
AU - Guivarch V.
AU - Gleizes M.
AU - Robert F.
PY - 2015
SP - 242
EP - 248
DO - 10.5220/0005282002420248