Abstract
Recently there has been a great interest in the design and study of evolvable and autonomous systems in order to control the behavior of physically embedded systems such as a mobile robot. This paper studies an evolutionary navigation system for a mobile robot using an evolvable hardware (EHW) approach. This approach is unique in that it combines learning and evolution, which was usually realized by software, with hardware. It can be regarded as an attempt to make hardware “softer”. The task of the mobile robot is to reach a goal represented by a colored ball while avoiding obstacles during its motion. We show that our approach can evolve a set of rules to perform the task successfully. We also show that the evolvable hardware system learned off-line is robust and able to perform the desired behaviors in a more complex environment which is not seen in the learning stage.
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
Randall D. Beer and J.C. Gallagher. Evolving dynamic neural networks for adaptive behavior. Adaptive Behavior, 1(1):91–122, July 1992.
Rodney Brooks. Artificial life and real robots. In F.J. Varela and Bourgine, editors, Proceedings of the First European Conference on Artificial Life, pages 3–10, Cambridge, MA, 1992. MIT Press/Bradford Books.
Dave Cliff, Inman Harvey, and Philip Husbands. Explorations in evolutionary robotics. Adaptive Behavior, 2(1):73–110, July 1993.
Jonalthan H. Connell and Sridhar Mahadevan. Robot Learning. Kluwer International Series in Engineering and Computer Science. Kluwer Academic Publisher, 1993.
Marco Dorigo and Marco Colombetti. Robot shaping: developing autonomous agents through learning. Artificial Intelligence, 71:321–370, 1994.
Marco Dorigo and Uwe Schnepf. Genetics-based machine learning and behavior-based robotics: A new synthesis. IEEE Transactions on Systems, Man, and Cybernetics, 23(1):141–154, 1993.
Dario Floreano and Francesco Mondada. Automatic creation of an autonomous agent: Genetic evolution of a neural-network driven robot. In J-A. Meyer Dave Cliff, Philip Husbands and S. Wilson, editors, From Animals to Animate 3: Proceedings of the 3rd International Conference on Simulation of Adaptive Behavior. MIT Press, 1994.
David E. Goldberg. Genetic Algorithms in search, optimization and machine learning. Addison Wesley, 1989.
John J. Grefenstette. Incremental learning of control strategies with genetic algorithms. In Proceedings of the Sixth International Workshop on Machine Learning, pages 340–344. Morgan Kaufmann, 1989.
John J. Grefenstette and A. Schultz. An evolutionary approach to learning in robots. In Proceedings of the Machine Learning Workshop on Robot Learning, Eleventh International Conference on Machine Learning. New Brunswick, NJ, 1994.
Inman Harvey, Philip Husbands, and Dave Cliff. Seeing the light: Artificial evolution, real vision. In J-A. Meyer Dave Cliff, Philip Husbands and S. Wilson, editors, From Animals to Animais 3: Proceedings of the 3rd International Conference on Simulation of Adaptive Behavior. MIT Press, 1994.
Tetsuya Higuchi, Tatsuya Niwa, Toshio Tanaka, Hitoshi Iba, Hugo de Garis, and T. Furuya. Evolvable hardware with genetic learning: A first step towards building a darwin machine. In Jean-Arcady Meyer, Herbert L. Roitblat, and Stewart W. Wilson, editors, Proceedings of the 2nd International Conference on the Simulation of Adaptive Behavior, pages 417–424. MIT Press, 1992.
Philip Husbands, Inman Harvey, Dave Cliff, and Geoffrey Miller. The use of genetic algorithms for the development of sensorimotor control systems. In F. Moran, A. Moreno, J.J. Merelo, and P. Chacon, editors, Proceedings of the third European Conference on Artificial Life, pages 110–121, Granada, Spain, 1995. Springer.
Leslie Pack Kaelbling. Learning in Embedded Systems. Bradford Book, MIT Press, Cambridge, 1993.
Didier Keymeulen, Marc Durantez, Kenji Konaka, Yasuo Kuniyoshi, and Tetsuya Higuchi. An evolutionary robot navigation system using a gate-level evolvable hardware. In Proceeding of the First International Conference on Evolvable Systems: from Biology to Hardware, pages 195–210. Springer Verlag, 1996.
Didier Keymeulen, Masaya Iwata, Kenji Konaka, Ryouhei Suzuki, Yasuo Kuniyoshi, and Tetsuya Higuchi. Off-line model-free and on-line model-based evolution for tracking navigation using evolvable hardware. In Proceeding of the First European Workshop on Evolutionary Robotics.Springer Verlag, 1998.
John Koza. Evolution of subsumption using genetic programming. In F. J. Varela and P. Bourgine, editors, Proceedings of the First European Conference on Artificial Life, pages 3–10, Cambridge, MA, 1992. MIT Press / Bradford Books.
Henrik Hautop Lund and John Hallam. Evolving sufficient robot controllers. In Proceedings of the International Conference on Evolutionary Computation, pages 495–499, Piscataway, NJ, 1997. IEEE.
Orazio Miglino, Henrik Hautop Lund, and Stefano Nolfi. Evolving mobile robots in simulated and real environments. Artificial Life, 2(4):417–434, summer 1995.
T. Nakamura and M. Asada. Stereo sketch: Stereo vision-based target reaching behavior acquisition with occlusion detection and avoidance. In Proceedings of LEEE International Conference on Robotics and Automation, pages 1314–1319. IEEE Press, 1996.
Balas Natarajan. Machine Learning: a theoretical approach. Morgan Kaufmann Publisher, 1991.
Peter Nordin and Wolfgang Banzhaf. An on-line method to evolve behavior and to control a miniature robot in real time with genetic programming. Adaptive Behavior, 5(2):107–140, 1997.
Domenico Parisi, Stefano Nolfi, and F. Cecconi. Learning, behavior and evolution. In Proceedings of the First European Conference on Artificial Life, pages 207–216, Cambridge, MA, 1992. MIT Press / Bradford Books.
Craig W. Reynolds. An evolved, vision-based model of obstacle avoidance behavior. In Artificial Life III, Sciences of Complexity, Proc. Vol. XVII, pages 327–346. Addison-Wesley, 1994.
Luc Steels and Rodney Brooks, editors. The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents. Lawrence Erlbaum Assoc, 1995.
Richard S. Sutton. Special issue on reinforcement learning. Machine Learning, 8(3/4), 1992.
Sebastian Thrun. Explanation-based Neural Network Learning: A Lifelong Learning Approach. Kluwer Academic Publishers, Boston, MA, 1996.
Eiji Uchibe, Minoru Asada, and Koh Hosoda. Behavior coordination for a mobile robot using modular reinforcement learning. In Proceedings of LEEE/RSJ International Conference on Intelligent Robotits and Systems (LROS 96), pages 1329–1336. IEEE Press, 1996.
Christopher J.C.H. Watkins and Peter Dayan. Q-learning. Machine Learning, 8(3):279–292, 1992.
Stewart Wilson. Classifier systems and the animat problem. Machine Learning, 2:199–228, 1987.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Keymeulen, D., Konaka, K., Iwata, M., Kuniyoshi, Y., Higuchi, T. (1998). Robot Learning using Gate-Level Evolvable Hardware. In: Birk, A., Demiris, J. (eds) Learning Robots. EWLR 1997. Lecture Notes in Computer Science(), vol 1545. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49240-2_12
Download citation
DOI: https://doi.org/10.1007/3-540-49240-2_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65480-3
Online ISBN: 978-3-540-49240-5
eBook Packages: Springer Book Archive