An On-chip Spiking Neural Network for Estimation of the Head Pose of the iCub Robot

• Published in: Frontiers in neuroscience Vol. 14; p. 551
• Main Authors: Kreiser, Raphaela, Renner, Alpha, Leite, Vanessa R. C., Serhan, Baris, Bartolozzi, Chiara, Glover, Arren, Sandamirskaya, Yulia
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 23-06-2020; Frontiers Media S.A
• Subjects: Brain research; Cameras; Distance learning; event-based vision; Firing pattern; Global positioning systems; GPS; Head; iCub robot; Integration; Neural networks; neuromorphic SLAM; Neuroscience; on-chip learning; pose estimation; Robotics; Robots; scene memory; Sensors; Synaptic plasticity; Velocity
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2020.00551

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, 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.