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As the use of collaborative robots (cobots) in industrial manufacturingcontinues to grow, human action recognition for effective human-robotcollaboration becomes increasingly important. This ability is crucial forcobots to act autonomously and assist in assembly tasks. Recently,skeleton-based approaches are often used as they tend to generalize better todifferent people and environments. However, when processing skeletons alone,information about the objects a human interacts with is lost. Therefore, wepresent a novel approach of integrating object information into skeleton-basedaction recognition. We enhance two state-of-the-art methods by treating objectcenters as further skeleton joints. Our experiments on the assembly datasetIKEA ASM show that our approach improves the performance of thesestate-of-the-art methods to a large extent when combining skeleton joints withobjects predicted by a state-of-the-art instance segmentation model. Ourresearch sheds light on the benefits of combining skeleton joints with objectinformation for human action recognition in assembly tasks. We analyze theeffect of the object detector on the combination for action classification anddiscuss the important factors that must be taken into account.

How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks