Geometric Reasoning enabled One Shot Learning for Robotic Tasks

Abstract

Flexible robotics will be a major enabling technology for the application of robot-based automation in other than traditionally suitable automotive or electronics production with high volumes. Increased demand for flexibility due to individualized production typical for most SMEs require an increased level of flexibility – also for robots that should be able to learn as well as provide an increased level of autonomy due to improved skills and extended reasoning capabilities. This publication tries to find out if novel ANN methodology that is able to process 3D surface data is applicable to generalize process knowledge in a one shot learning by demonstration situation in order to be able to execute tasks on similar but geometrically unequal objects in future settings. The methodology generalizes not on symbolic or trajectory level but on surface geometry level and was applied to a simple geometric object on lab scale. The algorithms introduced are applicable to more complex objects with practical relevance.