An Action Generation Model Using Time Series Prediction

Several studies have been made on intelligent systems typified by a robot. For these systems to behave appropriately in a complicated environment, an action generation model is necessary. This paper proposes an action generation model which consists of many motor primitive modules. The motor primitive modules output motor commands based on sensory information. Complicated behavior is generated by sequentially switching the modules. The model also has a prediction unit. This unit predicts which module will be used for current action generation. A current action is generated by the module chosen based on both the prediction and the current sensor input. Hence, the proposed model can produce different actions even when the current input information is the same. The proposed model is constructed by using a competitive neural network and a recurrent neural network. The modules and the prediction unit are acquired by learning from continuous sensory-motor flow. We have confirmed the effectiveness of the model by applying it to a robot navigation task simulation, and have investigated the influence of the prediction on the action generation.