A unified framework for imitation-like behaviors

摘要

In this paper, we combine the formal methods from reinforcement learning with the paradigm of imitation learning. The extension of the reinforcement learning framework to integrate the information provided by an expert (demonstrator) has the important advantage of allowing a clear decrease of the time necessary to learn
\ncertain robotic tasks. Hence, learning by imitation can be interpreted as a mechanism for fast skill transfer. Another contribution of thispaper consists in showing that our formalism is able to model different types of imitation-learning that are described in the biological literature. It thus unifies in the same abstract model what used to be
\naddressed as separate behavioral patterns. We illustrate the application of these methods in simulation and with a real robot.