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_posts/research/2018-11-01-trajectory-annotation.md
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---
layout: single
title: "Trajectory annotation by spatial perception"
categories: research
excerpt: "We propose an approach to annotate trajectories using sequences of spatial perception."
header:
teaser: assets/figures/0_trajectory_reconstruction_teaser.png
---
<figure class="half">
<img src="/assets/figures/0_trajectory_isovist.jpg" alt="" style="width:48%">
<img src="/assets/figures/0_trajectory_reconstruction.jpg" alt="" style="width:48%">
</figure>
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. {% cite feld2018trajectory %}