Linguistic Decision Making for Robot Route Learning

Machine learning enables the creation of a nonlinear mapping that describes robot-environment interaction, whereas computing linguistics make the interaction transparent. In this paper, we develop a novel application of a linguistic decision tree for a robot route learning problem by dynamically deciding the robot's behavior, which is decomposed into atomic actions in the context of a specified task. We examine the real-time performance of training and control of a linguistic decision tree, and explore the possibility of training a machine learning model in an adaptive system without dual CPUs for parallelization of training and control. A quantified evaluation approach is proposed, and a score is defined for the evaluation of a model's robustness regarding the quality of training data. Compared with the nonlinear system identification nonlinear auto-regressive moving average with eXogeneous inputs model structure with offline parameter estimation, the linguistic decision tree model with online linguistic ID3 learning achieves much better performance, robustness, and reliability.