  Human motion prediction is non-trivial in modern industrial settings.
Accurate prediction of human motion can not only improve efficiency in human
robot collaboration, but also enhance human safety in close proximity to
robots. Among existing prediction models, the parameterization and
identification methods of those models vary. It remains unclear what is the
necessary parameterization of a prediction model, whether online adaptation of
the model is necessary, and whether prediction can help improve safety and
efficiency during human robot collaboration. These problems result from the
difficulty to quantitatively evaluate various prediction models in a
closed-loop fashion in real human-robot interaction settings. This paper
develops a method to evaluate the closed-loop performance of different
prediction models. In particular, we compare models with different
parameterizations 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 prediction
significantly enhanced the collaboration efficiency and human safety. Adaptable
prediction models that were parameterized by neural networks achieved the best
performance.
