Abstract
We propose an exploratory form of motor babbling that uses variance predictions from a recurrent neural network as a method to acquire the body dynamics of a robot with flexible joints. In conventional research methods, it is difficult to construct real robots because of the large number of motor babbling motions required. In motor babbling, different motions may be easy or difficult to predict. The variance is large in difficult-to-predict motions, whereas the variance is small in easy-to-predict motions. We use a Stochastic Continuous Timescale Recurrent Neural Network to predict the accuracy and variance of motions. Using the proposed method, a robot can explore motions based on variance. To evaluate the proposed method, experiments were conducted in which the robot learns crank turning and door opening/closing tasks after exploring its body dynamics. The results show that the proposed method is capable of efficient motion generation for any given motion tasks.
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Acknowledgments
The work has been supported by the Program for Leading Graduate Schools, “Graduate Program for Embodiment Informatics” of the Ministry of Education, Culture, Sports, Science, and Technology; JSPS Grant-in-Aid for Scientific Research (S)(2522005); “Fundamental Study for Intelligent Machine to Coexist with Nature” Research Institute for Science and Engineering, Waseda University; MEXT Grant-in-Aid for Scientific Research (A) 15H01710; and Scientific Research on Innovative Areas “Constructive Developmental Science” 24119003.
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Takahashi, K., Suzuki, K., Ogata, T., Tjandra, H., Sugano, S. (2015). Efficient Motor Babbling Using Variance Predictions from a Recurrent Neural Network. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_4
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DOI: https://doi.org/10.1007/978-3-319-26555-1_4
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