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.
Bibliographical noteFunding Information:
The authors would like to thank the Applied Brain Research (ABR) team, and particularly to Travis DeWolf, for the support; to Intel labs for granting us access to their neuromorphic cloud and for the technical support; and to Timothy Shea from Accenture Labs and Tamara Pearlman Tsur for their insightful comments. Funding. This research was funded by Accenture Labs as part of Intel's INRC (Intel Neuromorphic Research Community) initiative and by the Open University of Israel research grant.
© Copyright © 2021 Zaidel, Shalumov, Volinski, Supic and Ezra Tsur.
- neural engineering framework
- neuromorphic engineering
- robotic arm
- robotic control software
- spiking neural networks