  Fluent and safe interactions of humans and robots require both partners to
anticipate the others' actions. A common approach to human intention inference
is to model specific trajectories towards known goals with supervised
classifiers. However, these approaches do not take possible future movements
into account nor do they make use of kinematic cues, such as legible and
predictable motion. The bottleneck of these methods is the lack of an accurate
model of general human motion. In this work, we present a conditional
variational autoencoder that is trained to predict a window of future human
motion given a window of past frames. Using skeletal data obtained from RGB
depth images, we show how this unsupervised approach can be used for online
motion prediction for up to 1660 ms. Additionally, we demonstrate online target
prediction within the first 300-500 ms after motion onset without the use of
target specific training data. The advantage of our probabilistic approach is
the possibility to draw samples of possible future motions. Finally, we
investigate how movements and kinematic cues are represented on the learned low
dimensional manifold.
