Abstract
This article describes research in which embodied imitation and behavioral adaptation are investigated in collective robotics. We model social learning in artificial agents with real robots. The robots are able to observe and learn each others' movement patterns using their on-board sensors only, so that imitation is embodied. We show that the variations that arise from embodiment allow certain behaviors that are better adapted to the process of imitation to emerge and evolve during multiple cycles of imitation. As these behaviors are more robust to uncertainties in the real robots' sensors and actuators, they can be learned by other members of the collective with higher fidelity. Three different types of learned-behavior memory have been experimentally tested to investigate the effect of memory capacity on the evolution of movement patterns, and results show that as the movement patterns evolve through multiple cycles of imitation, selection, and variation, the robots are able to, in a sense, agree on the structure of the behaviors that are imitated.
Highlights
This paper presents research on social learning in a group of robots
How do behaviours evolve and adapt as they undergo multiple cycles of embodied imitation and, in particular, do behaviours adapt to be better fitted to the ‘environment’ of the robot collective and the robots themselves? Second, we seek to understand how clusters of related behaviours arise and persist within the collective memory of the robot group; in particular we explore several approaches to the robots’ learnedbehaviour memory
3) Imitation with Limited Memory: In the previous set of experiments we saw that certain patterns, those that are more robust to uncertainties in the real robots’ sensors and the estimation process of imitation, can emerge during multiple cycles of imitation
Summary
This paper presents research on social learning in a group of robots. The work reported here was undertaken within a research project called “the emergence of artificial culture in robot societies” whose overall aim was to investigate the processes and mechanisms by which proto-cultural behaviours, better described as traditions, might emerge in a free running collective robot system. As the robots’ sensors and actuators are not perfect, even with a homogeneous group of real robots, variations occur during the imitation process that allow certain behavioural patterns to emerge and evolve during multiple cycles of imitation. These evolved behaviours can be imitated with higher fidelity as they are more robust to uncertainties in the real robots’ sensors and actuators; the behaviours have adapted to the robots, and the environment of the robot collective. The paper investigates the effect of different learned-behaviour memory sizes on the relatedness of the population of evolved behaviours across the whole collective
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