Visual Target Sequence Prediction via Hierarchical Temporal Memory Implemented on the iCub Robot

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9793)


In this article, we present our initial work on sequence prediction of a visual target by implementing a cortically inspired method, namely Hierarchical Temporal Memory (HTM). As a preliminary test, we employ HTM on periodic functions to quantify prediction performance with respect to prediction steps. We then perform simulation experiments on the iCub humanoid robot simulated in the Neurorobotics Platform. We use the robot as embodied agent which enables HTM to receive sequences of visual target position from its camera in order to predict target positions in different trajectories such as horizontal, vertical and sinusoidal. The obtained results indicate that HTM based method can be customized for robotics applications that require adaptation of spatiotemporal changes in the environment and acting accordingly.


Hierarchical Temporal Memory Sequence prediction Neurorobotics Platform 



The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project). The authors would like to thank the Italian Ministry of Foreign Affairs, General Directorate for the Promotion of the “Country System”, Bilateral and Multilateral Scientific and Technological Cooperation Unit, for the support through the Joint Laboratory on Biorobotics Engineering project.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.The BioRobotics Institute, Scuola Superiore Sant’AnnaPontederaItaly

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