Learning RNN-Based Gene Regulatory Networks for Robot Control

With the unique characteristic of orchestrating gene expression level in cellular metabolism during the development of living organisms, gene regulatory networks can be modeled as reliable and robust control mechanisms for robots. In this work we devise a recurrent neural network-based GRN model to control robots. To simulate the regulatory effects and make our model inferable from time-series data, we develop an enhanced learning algorithm, coupled with some heuristic techniques of data processing for performance improvement. We also establish a method of programming by demonstration to collect behavior sequence data of the robot as the expression profiles, and then employ our framework to infer controllers automatically. To verify the proposed approach, experiments have been conducted and the results show that our regulatory model can be inferred for robot control successfully.