Individual Human Behavior Identification Using an Inverse Reinforcement Learning Method

Shared control techniques have a great potential to create synergies in human-machine interaction for efficient and safe applications. However, an optimal interaction requires the machine to consider the individual behavior of the human partner. A widespread approach for modeling human behavior is given by optimal control theory, where the movement trajectories of a human arise from an optimized cost function. The aim of the identification is thus to determine parameters of a cost function which explains observed human motion. The central thesis of this paper is that individual cost function parameters which describe specific behavior can be determined by means of Inverse Reinforcement Learning. We show the applicability of the approach with a tracking control task example. The experiment consists in following a reference trajectory by means of a steering wheel. The study confirms that optimal control is suitable for modeling individual human behavior and demonstrates the suitability of Inverse Reinforcement Learning in order to determine the cost function parameters which explain measured data.