Principles and experimentations of self-organizing embedded agents allowing learning from demonstration in ambient robotics

Ambient systems are populated by many heterogeneous devices to provide adequate services to their users. The adaptation of an ambient system to the specific needs of its users is a challenging task. Because human-system interaction has to be as natural as possible, we propose an approach based on Learning from Demonstration (LfD). LfD is an interesting approach to generalize what has been observed during the demonstration to similar situations. However, using LfD in ambient systems needs adaptivity of the learning technique. We present ALEX, a multi-agent system able to dynamically learn and reuse contexts from demonstrations performed by a tutor. The results of the experiments performed on both a real and a virtual robot show interesting properties of our technology for ambient applications. Extreme Sensitive Robotic as a bottom-up approach to deal with complexity in ambient robotic.Self-adaptive multi-agent system for learning from demonstration in ambient robotic.Experiments show that tutorship learning with Context agent is promising.

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