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Human motion prediction is non-trivial in modern industrial settings.Accurate prediction of human motion can not only improve efficiency in humanrobot collaboration, but also enhance human safety in close proximity torobots. Among existing prediction models, the parameterization andidentification methods of those models vary. It remains unclear what is thenecessary parameterization of a prediction model, whether online adaptation ofthe model is necessary, and whether prediction can help improve safety andefficiency during human robot collaboration. These problems result from thedifficulty to quantitatively evaluate various prediction models in aclosed-loop fashion in real human-robot interaction settings. This paperdevelops a method to evaluate the closed-loop performance of differentprediction models. In particular, we compare models with differentparameterizations and models with or without online parameter adaptation.Extensive experiments were conducted on a human robot collaboration platform.The experimental results demonstrated that human motion predictionsignificantly enhanced the collaboration efficiency and human safety. Adaptableprediction models that were parameterized by neural networks achieved the bestperformance.

Experimental Evaluation of Human Motion Prediction: Toward Safe and Efficient Human Robot Collaboration