A Spiking Neural Network Mimics the Oculomotor System to Control a Biomimetic Robotic Head Without Learning on a Neuromorphic Hardware

Facilitated by the emergence of neuromorphic hardware, neuromorphic algorithms mimic the brain's asynchronous computation to improve energy efficiency, low latency, and robustness, which are crucial for a wide variety of real-time robotic applications. However, the limited on-chip learning abil...

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Bibliographic Details
Published in:IEEE transactions on medical robotics and bionics 2022-05, Vol.4 (2), p.520-529
Main Authors: Polykretis, Ioannis, Tang, Guangzhi, Balachandar, Praveenram, Michmizos, Konstantinos P.
Format: Article
Language:English
Subjects:
Algorithms
biologically-inspired robots
Biomimetics
Brain
Controllers
Energy efficiency
Hardware
Learning
Microprocessors
Neck
Neural networks
Neuromorphic computing
neuromorphic hardware
Neuromorphics
Neurons
Neurorobotics
Proportional integral derivative
Real time
Robot control
Robotics
Robots
Robustness (mathematics)
Saccadic eye movements
sensor-based control
Spiking
spiking neural network
Tracking
Training
Visualization
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Summary:Facilitated by the emergence of neuromorphic hardware, neuromorphic algorithms mimic the brain's asynchronous computation to improve energy efficiency, low latency, and robustness, which are crucial for a wide variety of real-time robotic applications. However, the limited on-chip learning abilities hinder the applicability of neuromorphic computing to real-world robotic tasks. Biomimetism can overcome this limitation by complementing or replacing training with the knowledge of the brain's connectome associated with the targeted behavior. By drawing inspiration from the human oculomotor network, we designed a spiking neural network (SNN) that tracked visual targets in real-time. We deployed the biomimetic controller on Intel's Loihi neuromorphic processor to control an in-house robotic head. The robot's behavior resembled the smooth pursuit and saccadic eye movements observed in humans, while the SNN on Loihi exhibited similar performance to a CPU-run PID controller. Interestingly, this behavior emerged from the SNN without training, which places the biomimetic design as an alternative to the energy- and data-greedy learning-based methods. This work reinforces our on-going efforts to devise energy-efficient autonomous robots that mimic the robustness and versatility of their biological counterparts.
ISSN:2576-3202
2576-3202
DOI:10.1109/TMRB.2022.3155278