@inproceedings {pub4388,
	title = {Gaze-based intention recognition for pick-and-place tasks in shared autonomy},
	author = {Stefan Fuchs AND Anna Belardinelli},
	year = {2020},
	abstract = { Shared autonomy aims at combining robotic and
human control in the execution of remote, teleoperated tasks.
This cooperative interaction cannot be brought about without
the robot first recognizing the current human intention in a fast
and reliable way, so that a suitable assisting plan can be quickly
instantiated and executed. Eye movements have long been
known to be highly predictive of the cognitive agenda unfolding
during manual tasks and constitute, hence, the earliest and most
reliable behavioral cues for intention estimation.
In this study, we present an experiment aimed at analyzing
human behavior in simple teleoperated pick-and-place tasks in
a simulated scenario and at devising a suitable model for early
estimation of the current proximal intention, that is either the
reaching target or the place-down location. We show that scan
paths are, as expected, heavily shaped by the current intention
and that a Gaussian Hidden Markov Model achieves a good
prediction performance, while also generalizing to a new object
configuration and new users. We finally discuss how behavioral
and model results suggest that eye movements reflect to some
extent the invariance and generality of higher level planning
across object configurations.},
	publisher = {university of Essex},
	booktitle = {Active Vision and perception in Human(-Robot) Collaboration Workshop @RO-MAN 2020 }
}