Neuromorphic control of a simulated 7-DOF arm using Loihi

Neuromorphic Computing and Engineering, 2023

Travis DeWolf, Kinjal Patel, Pawel Jaworski, Roxana Leontie, Joseph Hays, Chris Eliasmith


In this paper, we present a fully spiking neural network running on Intel’s Loihi chip for operational space control of a simulated 7-DOF arm. Our approach uniquely combines neural engineering and deep learning methods to successfully implement position and orientation control of the end effector. The development process involved 4 stages: 1) Designing a node-based network architecture implementing an analytical solution; 2) developing rate neuron networks to replace the nodes; 3) retraining the network to handle spiking neurons and temporal dynamics; and finally 4) adapting the network for the specific hardware constraints of the Loihi. We benchmark the controller on a center-out reaching task, using the deviation of the end effector from the ideal trajectory as our evaluation metric. The RMSE of the final neuromorphic controller running on Loihi is only slightly worse than the analytic solution, with 4.13

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Neuromorphic Computing and Engineering


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