Abstract
In this paper we present a developmental framework to carry out goal-oriented learning in a low-dimensional space. The framework uses two stages of learning: one to synthesise a set of motor synergies and reduce the dimensionality of the control space in an unsupervised manner, and another to carry out supervised learning in the reduced control space. We test our framework in a reaching task carried out on a (real) tendon-driven robot actuated by four artificial muscles. Our results show that the robot is capable of learning to reach using a reduced control space using no prior information about its body apart from that inherent to the unsupervised and supervised learning rules.
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Marques, H.G., Schaffner, P., Kuppuswamy, N. (2012). Unsupervised Learning of a Reduced Dimensional Controller for a Tendon Driven Robot Platform. In: Ziemke, T., Balkenius, C., Hallam, J. (eds) From Animals to Animats 12. SAB 2012. Lecture Notes in Computer Science(), vol 7426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33093-3_35
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DOI: https://doi.org/10.1007/978-3-642-33093-3_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33092-6
Online ISBN: 978-3-642-33093-3
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