Abstract
How to build distributed autonomous systems that can adaptively behave through learning in the real world is still an open problem in the research field. In order to tackle this problem, we constructed a distributed autonomous floating robot that consisted of mechanically linked multiple identical units and proposed a new control framework, adaptive oscillator method, to deal with units’ temporal and spatial interaction with their environment. A single model reinforcement learning system was first employed to realize the framework, and a multiple-model reinforcement learning system was proposed further and employed to cope with environmental changes caused by adding obstacles. In order to confirm adaptive behavior acquisition and fault-tolerance in a real environment, we did experiments on target approaching task by using the real floating robot.
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
Fukuda, T., Ueyama, T., Kawauchi, Y., and Arai, F. (1992). Concept of Cellular Robotic System (CEBOT) and Basic Strategies for its Realization, Computers Elect. Eng 18(1): 11–39, Pergamon Press.
Yim, M. (1993). A Reconfigurable Modular Robot with Many Modes of Locomotion, Proc. of the JSME Int. Conf. on Advanced Mechatronics, 283–288.
Kokaji, S., Murata, S., and Kurokawa, H. (1994). Self Organization of a Mechanical System, Distributed Autonomous Robotic Systems, 237–242.
Grillner, S. (1985). Neurobiological Bases of Rhythmic Motor Acts in Vertebrates, Science 228:143–149.
Hirose, S. (1993). Biologically Inspired Robots (Snake-Like Locomotors and Manipulators), Oxford Univ. Press.
Ijspeert, A.J., Hallam, J., and Willshaw, D. (1999). Evolving Swimming Controllers for a Simulated Lamprey with Inspiration from Neurobiology, Adaptive Behavior 7(2) 151–172.
Iijima, D., Yu, W., Yokoi, H., and Kakazu, Y. (1998). Autonomous Acquisition of Adaptive Behavior for a Distributed Floating Robot Based on the AHC Method, Intelligent Engineering Systems through Artificial Neural Networks 8:537–542.
Iijima, D., Yu, W., Yokoi, H., and Kakazu, Y. (1999). Autonomous Acquisition of Target Approaching Behavior for Distributed Controlled Swimming Robot (The Case of Presetting Oscillation Action Patterns), Trans. of the JSME 65(637):208–215.
Watkins, C.J.C.H., Dayan, P. (1993). Technical Note: Q-Learning, Sutton, Reinforcement Learning, 55–68.
Lin, L.J. (1993). Scaling Up Reinforcement Learning for Robot Control, Proc. of the 10th Int. Conf. on Machine Learning, 182–196.
Tani, J., and Nolfi, S. (1998). Learning to Perceive the World as Articulated: An Approach for Hierarchical Learning in Sensory-Motor Systems, From Animals To Animais 5, 270–279.
Taga, G. (1995). A Model of the Neuro-Musculo-Skeletal System for Human Locomotion II. Real-Time Adaptability under Various Constraints, Biol. Cybern. 73:113–121.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Tokyo
About this paper
Cite this paper
Yu, W., Takuya, I., Iijima, D., Yokoi, H., Kakazu, Y. (2002). Using Interaction-Based Learning to Construct an Adaptive and Fault-Tolerant Multi-Link Floating Robot. In: Asama, H., Arai, T., Fukuda, T., Hasegawa, T. (eds) Distributed Autonomous Robotic Systems 5. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65941-9_45
Download citation
DOI: https://doi.org/10.1007/978-4-431-65941-9_45
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-65943-3
Online ISBN: 978-4-431-65941-9
eBook Packages: Springer Book Archive