@article {pub4578,
	title = {Gaze-Based Intention Estimation for Shared Autonomy in Pick-and-Place Tasks},
	author = {Stefan Fuchs AND Anna Belardinelli},
	year = {2021},
	month = {April},
	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.
We show that scan paths are, as expected, heavily shaped by the current intention
and that two types of Gaussian Hidden Markov Models, one more scene-specific
and one more action-specific, achieve a very good prediction performance, while also
generalizing to new users and spatial arrangements. 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, which can be leveraged
by cooperative robotic systems.},
	publisher = {Frontiers},
	journal = {Frontiers in Neurorobotics},
	volume = {15},
	pages = {33}
}