Header

UZH-Logo

Maintenance Infos

Closed-Loop Spiking Control on a Neuromorphic Processor Implemented on the iCub


Zhao, Jingyue; Risi, Nicoletta; Monforte, Marco; Bartolozzi, Chiara; Indiveri, Giacomo; Donati, Elisa (2020). Closed-Loop Spiking Control on a Neuromorphic Processor Implemented on the iCub. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 10(4):546-556.

Abstract

Neuromorphic engineering promises the deployment of low latency, adaptive and low power systems that can lead to the design of truly autonomous artificial agents. However, many building blocks for developing a fully neuromorphic artificial agent are still missing. While neuromorphic sensing, perception, and decision-making building blocks are quite mature, the ones for motor control and actuation are lagging behind. In this paper we present a closed-loop motor controller implemented on a mixed-signal analog/digital neuromorphic processor which emulates a spiking neural network that continuously calculates an error signal from the desired target and the feedback signals. The system uses population coding and recurrent Winner-Take-All networks to encode the signals robustly. Recurrent connections within each population are used to speed up the convergence, decrease the effect of mismatch and improve selectivity. The error signal computed in this way is then fed into three additional populations of spiking neurons which produce the proportional, integral and derivative terms of classical controllers exploiting the temporal dynamics of the network synapses and neurons. To validate this approach we interfaced this neuromorphic motor controller with an iCub robot simulator. We tested our spiking controller in a single joint control task for the robot head yaw. We demonstrate the correct performance of the spiking controller in a step response experiment and apply it to a target pursuit task.

Abstract

Neuromorphic engineering promises the deployment of low latency, adaptive and low power systems that can lead to the design of truly autonomous artificial agents. However, many building blocks for developing a fully neuromorphic artificial agent are still missing. While neuromorphic sensing, perception, and decision-making building blocks are quite mature, the ones for motor control and actuation are lagging behind. In this paper we present a closed-loop motor controller implemented on a mixed-signal analog/digital neuromorphic processor which emulates a spiking neural network that continuously calculates an error signal from the desired target and the feedback signals. The system uses population coding and recurrent Winner-Take-All networks to encode the signals robustly. Recurrent connections within each population are used to speed up the convergence, decrease the effect of mismatch and improve selectivity. The error signal computed in this way is then fed into three additional populations of spiking neurons which produce the proportional, integral and derivative terms of classical controllers exploiting the temporal dynamics of the network synapses and neurons. To validate this approach we interfaced this neuromorphic motor controller with an iCub robot simulator. We tested our spiking controller in a single joint control task for the robot head yaw. We demonstrate the correct performance of the spiking controller in a step response experiment and apply it to a target pursuit task.

Statistics

Citations

Altmetrics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Electrical and Electronic Engineering
Uncontrolled Keywords:Electrical and Electronic Engineering
Language:English
Date:1 December 2020
Deposited On:16 Feb 2021 08:53
Last Modified:17 Feb 2021 21:02
Publisher:Institute of Electrical and Electronics Engineers
ISSN:2156-3357
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/jetcas.2020.3040390
Project Information:
  • : FunderH2020
  • : Grant ID753470
  • : Project TitleNEPSpiNN - Neuromorphic EMG Processing with Spiking Neural Networks
  • : FunderH2020
  • : Grant ID724295
  • : Project TitleNeuroAgents - Neuromorphic Electronic Agents: from sensory processing to autonomous cognitive behavior

Download

Full text not available from this repository.
View at publisher

Get full-text in a library