Towards robust intention estimation based on object affordance enabling natural human-robot collaboration in assembly tasks

Abstract

In manufacturing industry, a shift is observable towards high-mix, low-volume batches. The upcoming era demands both flexible and automated systems for which the required skills go far beyond those of humans or robots alone. The solution can be found in human-robot collaboration (HRC). HRC could be more natural and efficient if both actors recognise and anticipate each other’s activities and intentions. Current mainstream research focuses on recognising activities using a machine learning framework trained with labelled data of movements and gestures. This approach ignores the object affordances: the valuable relationships between the activities and objects being manipulated. In this paper, first steps are made towards an alternative approach where object affordances are adopted to recognise operator activities and intentions during an HRC assembly task.