  Accurate prediction of human movements is required to enhance the efficiency
of physical human-robot interaction. Behavioral differences across various
users are crucial factors that limit the prediction of human motion. Although
recent neural network-based modeling methods have improved their prediction
accuracy, most did not consider an effective adaptations to different users,
thereby employing the same model parameters for all users. To deal with this
insufficiently addressed challenge, we introduce a meta-learning framework to
facilitate the rapid adaptation of the model to unseen users. In this study, we
propose a model structure and a meta-learning algorithm specialized to enable
fast user adaptation in predicting human movements in cooperative situations
with robots. The proposed prediction model comprises shared and adaptive
parameters, each addressing the user's general and individual movements. Using
only a small amount of data from an individual user, the adaptive parameters
are adjusted to enable user-specific prediction through a two-step process:
initialization via a separate network and adaptation via a few gradient steps.
Regarding the motion dataset that has 20 users collaborating with a robotic
device, the proposed method outperforms existing meta-learning and
non-meta-learning baselines in predicting the movements of unseen users.
