1. Feld, S., Illium, S., Sedlmeier, A., and Belzner, L. 2018. Trajectory annotation using sequences of spatial perception. Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 329–338.

This work establishes a foundation for enhancing interaction between robots and humans in shared spaces by developing reliable systems for verbal communication. It introduces an unsupervised learning method using neural autoencoding to learn continuous spatial representations from trajectory data, enabling clustering of movements based on spatial context. The approach yields semantically meaningful encodings of spatio-temporal data for creating prototypical representations, setting a promising direction for future applications in robotic-human interaction. [Feld et al. 2018]