Robot self-preservation and adaptation to user preferences in game play, a preliminary study

Abstract

It is expected that in a near future, personal robots will be endowed with enough autonomy to function and live in an individual's home. This is while commercial robots are designed with default configuration and factory settings which may often be different to an individual's operating preferences. This paper presents how reinforcement learning is applied and utilised towards personalisation of a robot's behaviour. Two-level reinforcement learning has been implemented: first level is in charge of energy autonomy, i.e. how to survive, and second level is involved in adapting robot's behaviour to user's preferences. In both levels Q-learning algorithm has been applied. First level actions have been learnt in a simulated environment and then the results have been transferred to the real robot. Second level has been fully implemented in the real robot and learnt by human-robot interaction. Finally, experiments showing the performance of the system are presented.