Increasing Learning Speed by Imitation in Multi-robot Societies

The paradigm of imitation provides a powerful means for increasing the overall learning speed in a group of robots. While separately exploring the environment in order to learn how to behave with respect to a pre-defined goal, a robot gathers experience based on its own actions and interactions with the surroundings, respectively. By accumulating additional experience via observing the behaviour of other robots, the learning process can be significantly improved in terms of speed and quality. Within this article we present an approach, that enables robots in a multi-robot society to imitate any other available robot without imposing unnecessary restrictions regarding the robots’ design. Therefore, it benefits not only from its own actions, but also from actions that an observed robot performs. In order to realise the imitation paradigm, we solve three main challenges, namely enabling a robot to decide whom and when to imitate, to interpret and thereby understand the behaviour of an observed robot, and to integrate the experience gathered by observation into its individual learning process.