Abstract
A method of generating new motions associatively from novel trajectories that the robot receives is described. The associative motion generation system is composed of two neural networks: nonlinear principal component analysis (NLPCA) and Jordan recurrent neural network (JRNN). First, these networks learn the relationship between a trajectory and a motion using training data. Second, associative values are extracted for associating a new corresponding motion from a new trajectory using NLPCA. Finally, a new motion is generated through calculation by JRNN using the associative values. Experimental results demonstrated that our method enabled a humanoid robot, KHR-2HV, to associatively generate the new motions corresponding to trajectories that the robot had not learned.
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Motomura, S., Kato, S., Itoh, H. (2009). Generating Association-Based Motion through Human-Robot Interaction. In: Yang, JJ., Yokoo, M., Ito, T., Jin, Z., Scerri, P. (eds) Principles of Practice in Multi-Agent Systems. PRIMA 2009. Lecture Notes in Computer Science(), vol 5925. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11161-7_27
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DOI: https://doi.org/10.1007/978-3-642-11161-7_27
Publisher Name: Springer, Berlin, Heidelberg
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