An action generation model by using time series prediction and its application to robot navigation.
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. 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.
Gouko
Manabu
M
Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, 226-8502, Japan. gouko@dis.titech.ac.jp
Ito
Koji
K
D000818:Animals; D003198:Computer Simulation; D008954:Models, Biological; D009043:Motor Activity / Q000502:physiology*; D016571:Neural Networks, Computer; D011323:Primates; D012371:Robotics; D013037:Spatial Behavior / Q000502:physiology*; D013997:Time Factors