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

Abstract

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.

Self-organizing multi-agent systems for the control of complex systems

“…Other AMASs have tackled the problem of learning and control with similar Context Agents, for instance with model generation ( Nigon et al, 2016 ) and ambient robotics ( Verstaevel et al, 2016 ). Context Agents are being generalized and standardized to become a pattern for context learning in a multi-agent system ( Boes et al, 2015 ).…”

Process of Learning from Demonstration with Paraconsistent Artificial Neural Cells for Application in Linear Cartesian Robots

“…The research related to the application of this technique aims to find efficient ways in which to replace the manual programming work of machine activities by an automatic programming process, one that is exclusively conducted to repeat the task demonstrated by an expert [8,9]. As seen in [10], in the fundamental concepts of Lf D usage in robotics area, there is a problem of imitation as an entity tries to produce a behavior similar to another entity [9,10]. Thus, a Tutor evolving in a world realizes an observation Ω of this world.…”

“…In this way, the Tutor demonstrating a particular behavior can observe modifications of the world that the Learner cannot perceive. Therefore, with the comparisons of information data between the Tutor and the system contaminated with contradictions, thus generating uncertainties in the conclusions, the Lf D process can reach a low level of efficiency [10,11].…”

“…With the values obtained in Equations ( 8) and ( 9) we can represent the learning state of lPANCell by Equation (10):…”

Creating an ambient intelligence network using insight and merged reality technologies

“…To explore the central ideas in this paper several robots (see figures 1 and 2) and devices were built with various sensors and capabilities, to implement the idea of ambient robotics [8] [9]. A basic robot type consisted of a two-wheel base, motor driver unit, CPU card (Arduino based), a prototyping card and a WIFI card.…”