  In the near future, more and more machines will perform tasks in the vicinity
of human spaces or support them directly in their spatially bound activities.
In order to simplify the verbal communication and the interaction between
robotic units and/or humans, reliable and robust systems w.r.t. noise and
processing results are needed. This work builds a foundation to address this
task. By using a continuous representation of spatial perception in interiors
learned from trajectory data, our approach clusters movement in dependency to
its spatial context. We propose an unsupervised learning approach based on a
neural autoencoding that learns semantically meaningful continuous encodings of
spatio-temporal trajectory data. This learned encoding can be used to form
prototypical representations. We present promising results that clear the path
for future applications.
