Visually-guided adaptive robot (ViGuAR)

Abstract

A neural modeling platform known as Cog ex Machina <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> (Cog) developed in the context of the DARPA SyNAPSE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> program offers a computational environment that promises, in a foreseeable future, the creation of adaptive whole-brain systems subserving complex behavioral functions in virtual and robotic agents. Cog is designed to operate on low-powered, extremely storage-dense memristive hardware <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> that would support massively-parallel, scalable computations. We report an adaptive robotic agent, ViGuAR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> , that we developed as a neural model implemented on the Cog platform. The neuromorphic architecture of the ViGuAR brain is designed to support visually-guided navigation and learning, which in combination with the path-planning, memory-driven navigation agent - MoNETA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> - also developed at the Neuromorphics Lab at Boston University, should effectively account for a wide range of key features in rodents' navigational behavior.