Emergent neural turing machine and its visual navigation

Abstract

Traditional Turing Machines (TMs) are symbolic whose hand-crafted representations are static and limited. Developmental Network 1 (DN-1) uses emergent representation to perform Turing Computation. But DN-1 lacks hierarchy in its internal representations, and is hard to handle the complex visual navigation tasks. In this paper, we improve DN-1 with several new mechanisms and presenta new emergent neural Turing Machine - Developmental Network 2 (DN-2). By neural, we mean that the control of the TM has neurons as basic computing elements. The major novelty of DN-2 over DN-1 is that the representational hierarchy inside DN-2 is emergent and fluid. DN-2 grows complex hierarchies by dynamically allowing initialization of neurons with different ranges of connection. We present a complex task - vision guided navigation in simulated and natural worlds using DN-2. A major function that has not been demonstrated before is that the hierarchy enables attention that disregards distracting features based on the navigation context. In simulated navigation experiments, DN-2 can perform with no errors, and in real-world navigation experiments, the error rate is only 0.78%. These experimental results showed that DN-2 successfully learned rules of navigation with image inputs.

Keywords: Autonomous navigation; General-purpose visual learning; Hierarchical representation; Neural network; Universal turing machine.