Abstract
Neuromorphic hardware has several promising advantages compared to von Neumann architectures and is highly interesting for robot control. However, despite the high speed and energy efficiency of neuromorphic computing, algorithms utilizing this hardware in control scenarios are still missing. One problem is the transition from fast spiking activity on the hardware, which acts on a timescale of a few milliseconds, to a control-relevant timescale on the order of hundreds of milliseconds. Another problem is to enable the execution of complex trajectories, requiring the spiking activity to contain sufficient variability, while at the same time, for reliable performance, network dynamics require adequate robustness against noise. In this study we exploit a recently developed biologically-inspired spiking neural network model, the so-called anisotropic network, as the basis for a neuromorphic algorithm for robotic control. For this, we identified and transferred the core principles of the anisotropic network to neuromorphic hardware using Intel's neuromorphic research chip Loihi and validated the system on trajectories from a motor-control task performed by a robot arm. We show that the anisotropic network on Loihi reliably encodes sequential patterns of neural activity, each representing a robotic action, and that the patterns allow the generation of multidimensional trajectories on control-relevant timescales. Taken together, our study presents a new algorithm that allows the control of complex robotic movements using state of the art neuromorphic hardware.
Abstract (translated)
URL
https://arxiv.org/abs/2008.11642
