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.
Highlights
Neuromorphic hardware implements the non-Von Neumann brain-inspired computing architecture based on known properties of biological neural networks
We describe experiments in which the proposed spiking neural network (SNN) model estimates the head pose of the iCub robot
We assessed the accuracy of the integration component of the head-direction SNN without visual input
Summary
Neuromorphic hardware implements the non-Von Neumann brain-inspired computing architecture based on known properties of biological neural networks. This computing architecture features event-based asynchronous processing and fine-grained parallelism of a network of spiking neurons (Indiveri et al, 2009; Schemmel et al, 2010; Furber et al, 2012; Merolla et al, 2014; Galluppi et al, 2015; Qiao et al, 2015; Davies et al, 2018; Moradi et al, 2018). We contribute to the emerging field of neuromorphic robotics by presenting a number of design patterns—spiking neural network models—to solve one of the key robotic tasks, state estimation
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