Abstract
Online robot learning has been a goal for researchers for several decades. A problem arises when learning algorithms need to explore the environment as actions cannot easily be anticipated. Because of this, safety is a major issue when using learning algorithms.
This paper presents a framework for safe robot learning by the use of region-classification and energy limitation. The main task of the framework is to ensure safety regardless of a learning algorithm’s input to a system. This is necessary to allow a learning robot to explore environments without damaging itself or its surroundings. To ensure safety, the state-space is divided into fatal, supercritical, critical and safe regions, depending on the energy of the system.
To show the adaptability of the framework it is used on two different systems; an actuated swinging pendulum and a mobile platform. In both cases obstacles with unknown locations must are avoided successfully.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Connell, J.H., Mahadevan, S.: Introduction to Robot Learning. Springer (1993)
Olivier Chapelle, A.Z., Schölkopf, B.: Semi-Supervised Learning. The MIT Press (2006)
Gelly, S., Silver, D.: Combining online and offline knowledge in uct. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. 273–280. ACM, New York (2007)
Sutton, R.S., Barto, A.G.: Reinforcement learning. Journal of Cognitive Neuroscience 11(1), 126–130 (1999)
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: A survey. Journal of Artificial Intelligence Research 4, 237–285 (1996)
Gillula, J.H., Tomlin, C.J.: Guaranteed safe online learning of a bounded system. In: IROS 2011, pp. 2979–2984 (September 2011)
Hans, A., Schneegaß, D., Schäfer, A.M., Udluft, S.: Safe exploration for reinforcement learning. In: European Symposium on Artificial Neural Network, pp. 143–148 (April 2008)
Fjerdingen, S.A., Kyrkjebø, E.: Safe reinforcement learning for continuous spaces through Lyapunov-constrained behavior. In: Frontiers in Artificial Intelligence and Applications, pp. 70–79 (May 2011)
Perkins, T.J., Barto, A.G.: Lyapunov design for safe reinforcement learning. J. Mach. Learn. Res. 3, 803–832 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Albrektsen, S.M., Fjerdingen, S.A. (2012). Safe Robot Learning by Energy Limitation. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7508. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33503-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-33503-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33502-0
Online ISBN: 978-3-642-33503-7
eBook Packages: Computer ScienceComputer Science (R0)