Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor

Glatz, Sebastian; Martel, Julien N. P.; Kreiser, Raphaela; Qiao, Ning; Sandamirskaya, Yulia

doi:10.48550/arxiv.1810.10801

Cited by 1 publication

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“…A different approach consists in the use of SNN to implement the traditional Proportional Integral Derivative (PID) controller. Different attempts have been made in this direction: on FPGAs [8] with the neuromorphic digital processor SpiNNaker [9], and on a mixed analog-digital device [10]. In the latter, the SNN is in charge of the error calculation and the motor command is encoded in the firing rate of the output population.…”

Section: Introductionmentioning

confidence: 99%

Closed-loop spiking control on a neuromorphic processor implemented on the iCub

Zhao,

Risi,

Monforte

et al. 2020

Preprint

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Despite neuromorphic engineering promises the deployment of low latency, adaptive and low power systems that can lead to the design of truly autonomous artificial agents, the development of a fully neuromorphic artificial agent is still missing. While neuromorphic sensing and perception, as well as decision-making systems, are now mature, the control and actuation part is lagging behind. In this paper, we present a closed-loop motor controller implemented on mixed-signal analog-digital neuromorphic hardware using a spiking neural network. The network performs a proportional control action by encoding target, feedback, and error signals using a spiking relational network. It continuously calculates the error through a connectivity pattern, which relates the three variables by means of feed-forward connections. Recurrent connections within each population are used to speed up the convergence, decrease the effect of mismatch and improve selectivity. The neuromorphic motor controller is interfaced with the iCub robot simulator. We tested our spiking P controller in a single joint control task, specifically for the robot head yaw. The spiking controller sends the target positions, reads the motor state from its encoder, and sends back the motor commands to the joint. The performance of the spiking controller is tested in a step response experiment and in a target pursuit task. In this work, we optimize the network structure to make it more robust to noisy inputs and device mismatch, which leads to better control performances.

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Section: Introductionmentioning

confidence: 99%