Identifying relevant feature-action associations for grasping unmodelled objects

Abstract

AbstractAction affordance learning based on visual sensory information is a crucial problem within the development of cognitive agents. In this paper, we present a method for learning action affordances based on basic visual features, which can vary in their granularity, order of combination and semantic content. The method is provided with a large and structured set of visual features, motivated by the visual hierarchy in primates and finds relevant feature action associations automatically. We apply our method in a simulated environment on three different object sets for the case of grasp affordance learning. For box objects,we achieve a 0.90 success probability, 0.80 for round objects and up to 0.75 for open objects, when presented with novel objects. In thiswork,we demonstrate, in particular, the effect of choosing appropriate feature representations. We demonstrate a significant performance improvement by increasing the complexity of the perceptual representation. By that, we present important insights in how the design of the feature space influences the actual learning problem.