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Layered understanding for sporadic imitation in a multi-robot scenario

With imitation robots have a powerful means to drastically cut down the exploration space. However, as existing imitation approaches usually require repetitive demonstrations of the skill to learn in order to be useful, those are typically not applicable in groups of robots. In these scenarios usually each robot has its own task to accomplish and should not be disturbed by teaching others. Therefor, most of the time an imitating robot has only one observed performance of the behavior from which it can learn. Utilisation of these sparse observation data has largely been ignored. We present an approach that allows an individually learning robot to make use of such cases of sporadic imitation which is often the only possibility to learn from other robots in a group. The power of the algorithm comes from the fact that it uses the robots already known skills and strategies to understand the observed behavior. Thereby, a robot can use imitation in order to guide its exploration efforts towards more rewarding areas in the exploration space. This is inspired by imitation often found in nature where animals or humans try to map observations into their own capability space. We show the feasibility by realistic simulation of Pioneer robots.