Combining GRN Modeling and Demonstration-Based Programming for Robot Control

Gene regulatory networks dynamically orchestrate the level of expression for each gene in the genome. With such unique characteristics, they 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 verily the proposed approach, experiments have been conducted and the results show that our regulatory model can be inferred for robot control successfully.