Trajectory annotation using sequences of spatial perception
This work addresses the need for reliable and robust systems for human-robot interaction in spatially bound activities, though it appears incremental as it builds a foundation without claiming major breakthroughs.
The paper tackles the problem of simplifying verbal communication and interaction between robots and humans in shared spaces by developing an unsupervised learning approach that clusters movement based on spatial context using continuous representations of spatial perception learned from trajectory data. The approach produces semantically meaningful encodings and prototypical representations, with promising results that pave the way for future applications.
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.