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Associative motion generation for humanoid robots based on analogy with indication

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3 Author(s)
Satona Motomura ; Dept. of Computer Science and Engineering, Nagoya Institute of Technology. Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan ; Shohei Kato ; Hidenori Itoh

We describe a method of generating new motions associatively from unfamiliar indications. The associative motion generation system is composed of two neural networks: nonlinear principal component analysis (NLPCA) and Jordan recurrent neural network (JRNN). First, the system learns the correspondence relationship between an indication and a motion using training data. Second, associative values are extracted for associating a new motion from an unfamiliar indication using NLPCA. Last, the robot generates a new motion through calculation by JRNN using the associative values. Experimental results demonstrated that our method enabled a humanoid robot, KHR-2HV, to associatively generate some kinds of motion depending on given unfamiliar indications.

Published in:

Micro-NanoMechatronics and Human Science, 2009. MHS 2009. International Symposium on

Date of Conference:

9-11 Nov. 2009