Summary
In this chapter, we present a motion generation and adaptation method for dynamical motion. First we introduce a self-scaling reinforcement learning algorithm for fuzzy controllers and we apply it to the control of a robot with a single actuator. We then extend the controller and the learning algorithm in order to control a robot with multiple degrees of freedom. In this case, a hierarchical behavior-based controller architecture is used to simplify the design process. Finally we introduce the Newton Raphson method so as to adjust a hierarchical behavior-based controller through an on-line learning process to some modifications in the environment or task. The methods proposed are applied to a brachiation robot control, both in numerical simulations and in real experiments. This brachiation robot has been developed to imitate a long-armed ape.
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Fukuda, T., Hasegawa, Y. (2003). Behavior Coordination and its Modification on Monkey-type Mobile Robot. In: Duro, R.J., Santos, J., Graña, M. (eds) Biologically Inspired Robot Behavior Engineering. Studies in Fuzziness and Soft Computing, vol 109. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1775-1_2
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DOI: https://doi.org/10.1007/978-3-7908-1775-1_2
Publisher Name: Physica, Heidelberg
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