Neuromorphic NEF-Based Inverse Kinematics and PID Control

Yuval Zaidel, Albert Shalumov, Alex Volinski, Lazar Supic, Elishai Ezra Tsur

Research output: Contribution to journalArticlepeer-review


Neuromorphic implementation of robotic control has been shown to outperform conventional control paradigms in terms of robustness to perturbations and adaptation to varying conditions. Two main ingredients of robotics are inverse kinematic and Proportional–Integral–Derivative (PID) control. Inverse kinematics is used to compute an appropriate state in a robot's configuration space, given a target position in task space. PID control applies responsive correction signals to a robot's actuators, allowing it to reach its target accurately. The Neural Engineering Framework (NEF) offers a theoretical framework for a neuromorphic encoding of mathematical constructs with spiking neurons for the implementation of functional large-scale neural networks. In this work, we developed NEF-based neuromorphic algorithms for inverse kinematics and PID control, which we used to manipulate 6 degrees of freedom robotic arm. We used online learning for inverse kinematics and signal integration and differentiation for PID, offering high performing and energy-efficient neuromorphic control. Algorithms were evaluated in simulation as well as on Intel's Loihi neuromorphic hardware.

Original languageEnglish
Article number631159
JournalFrontiers in Neurorobotics
StatePublished - 3 Feb 2021


  • Loihi
  • neural engineering framework
  • neuromorphic engineering
  • robotic arm
  • robotic control software
  • spiking neural networks

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Artificial Intelligence


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