Kreiser, Raphaela, Renner, Alpha, Leite, Vanessa R. C. , Serhan, Baris, Bartolozzi, Chiara, Glover, Arren and Sandamirskaya, Yulia (2020) An On-chip Spiking Neural Network for Estimation of the Head Pose of the iCub Robot. Frontiers in Neuroscience, 14 . ISSN 1662-453X
Full content URL: https://doi.org/10.3389/fnins.2020.00551
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fnins-14-00551.pdf - Whole Document Available under License Creative Commons Attribution 4.0 International. 4MB |
Item Type: | Article |
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Item Status: | Live Archive |
Abstract
In this work, we present a neuromorphic architecture for head pose estimation and scene representation for the humanoid iCub robot. The spiking neuronal network is fully realized in Intel's neuromorphic research chip, Loihi, and precisely integrates the issued motor commands to estimate the iCub's head pose in a neuronal path-integration process. The neuromorphic vision system of the iCub is used to correct for drift in the pose estimation. Positions of objects in front of the robot are memorized using on-chip synaptic plasticity. We present real-time robotic experiments using 2 degrees of freedom (DoF) of the robot's head and show precise path integration, visual reset, and object position learning on-chip. We discuss the requirements for integrating the robotic system and neuromorphic hardware with current technologies.
Keywords: | pose estimation, event-based vision, neuromorphic SLAM, on-chip learning, scene memory, iCub robot, visual reset |
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Subjects: | H Engineering > H671 Robotics |
Divisions: | College of Science > School of Computer Science |
ID Code: | 41311 |
Deposited On: | 08 Jul 2020 15:59 |
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