Inference of Multi-Class STL Specifications for Multi-Label Human-Robot Encounters
Alexis Linard, Ilaria Torre, Iolanda Leite, Jana Tumova
Abstract
This paper is interested in formalizing human trajectories in human-robot encounters. Inspired by robot navigation tasks in human-crowded environments, we consider the case where a human and a robot walk towards each other, and where humans have to avoid colliding with the incoming robot. Further, humans may describe different be-haviors, ranging from being in a hurry/minimizing completion time to maximizing safety. We propose a decision tree-based algorithm to extract STL formulae from multi-label data. Our inference algorithm learns STL specifications from data containing multiple classes, where instances can be labelled by one or many classes. We base our evaluation on a dataset of trajectories collected through an online study reproducing human-robot encounters.
BibTeX
@inproceedings{iros2022_inferenceofmulti,
title = {Inference of Multi-Class STL Specifications for Multi-Label Human-Robot Encounters},
author = {Alexis Linard and Ilaria Torre and Iolanda Leite and Jana Tumova},
booktitle = {IROS 2022},
year = {2022}
}