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Combining GRN modeling and demonstration-based programming for robot control

  • EANN 2009
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Abstract

In recent years, gene regulatory networks (GRNs) have been proposed to work as reliable and robust control mechanisms for robots. Because recurrent neural networks (RNNs) have the unique characteristic of presenting system dynamics over time, we thus adopt such kind of network structure and the principles of gene regulation to develop a biologically and computationally plausible GRN model for robot control. To simulate the regulatory effects and to make our model inferable from time-series data, we also implement an enhanced network-learning algorithm to derive network parameters efficiently. In addition, we present a procedure of programming-by-demonstration to collect behavior sequence data of the robot as expression profiles, and then employ our network-modeling framework to infer controllers. To verify the proposed approach, experiments have been conducted, and the results show that our regulatory model can be inferred for robot control successfully.

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Acknowledgments

This work was supported by National Science Council of Taiwan, under contract NSC 96-2221-E-110-081.

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Authors and Affiliations

  1. Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan

    Wei-Po Lee & Tsung-Hsien Yang

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  1. Wei-Po Lee
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  2. Tsung-Hsien Yang
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Correspondence to Wei-Po Lee.

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Lee, WP., Yang, TH. Combining GRN modeling and demonstration-based programming for robot control. Neural Comput & Applic 20, 909–921 (2011). https://doi.org/10.1007/s00521-010-0496-z

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