Real-world reinforcement learning for autonomous humanoid robot charging in a home environment

Authors:
Nicolás Navarro;Cornelius Weber;Stefan Wermter
Affiliations:
Department of Computer Science, University of Hamburg, Hamburg, Germany;Department of Computer Science, University of Hamburg, Hamburg, Germany;Department of Computer Science, University of Hamburg, Hamburg, Germany
Venue:
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Year:
2011

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Abstract

In this paper we investigate and develop a real-world reinforcement learning approach to autonomously recharge a humanoid Nao robot [1]. Using a supervised reinforcement learning approach, combined with a Gaussian distributed states activation, we are able to teach the robot to navigate towards a docking station, and thus extend the duration of autonomy of the Nao by recharging. The control concept is based on visual information provided by naomarks and six basic actions. It was developed and tested using a real Nao robot within a home environment scenario. No simulation was involved. This approach promises to be a robust way of implementing real-world reinforcement learning, has only few model assumptions and offers faster learning than conventional Q-learning or SARSA.