Learning the Selection of Actions for an Autonomou

Authors: Álvaro CastroGonzález María Malfaz Miguel A Salichs

Publish Date: 2011/09/29

Volume: 3, Issue: 4, Pages: 427-441

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Abstract

Autonomy is a prime issue on robotics field and it is closely related to decision making Last researches on decision making for social robots are focused on biologically inspired mechanisms for taking decisions Following this approach we propose a motivational system for decision making using internal drives and external stimuli for learning to choose the right action Actions are selected from a finite set of skills in order to keep robot’s needs within an acceptable range The robot uses reinforcement learning in order to calculate the suitability of every action in each state The state of the robot is determined by the dominant motivation and its relation to the objects presents in its environmentThe used reinforcement learning method exploits a new algorithm called Object QLearning The proposed reduction of the state space and the new algorithm considering the collateral effects relationship between different objects results in a suitable algorithm to be applied to robots living in real environmentsIn this paper a first implementation of the decision making system and the learning process is implemented on a social robot showing an improvement in robot’s performance The quality of its performance will be determined by observing the evolution of the robot’s wellbeing

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