@inproceedings {pub4840pub4993,
	title = {Intention estimation from gaze and motion features for human-robot
shared-control object manipulation},
	author = {Anna Belardinelli AND Dirk Ruiken AND Daniel Tanneberg},
	year = {2022},
	month = {October},
	abstract = { Shared control can help in teleoperated object
manipulation by assisting with the execution of the user{\textquoteright}s
intention. To this end, robust and prompt intention estimation
is needed, which relies on behavioral observations. Here, an
intention estimation framework is presented, which uses natural
gaze and motion features to predict the current action and
the target object. The system is trained and tested in a
simulated environment with pick and place sequences produced
in a relatively cluttered scene and with both hands, with
possible hand-over to the other hand. Validation is conducted
across different users and hands, achieving good accuracy and
earliness of prediction. An analysis of the predictive power
of single features shows the predominance of the grasping
trigger and the gaze features in the early identifi cation of the
current action. In the current framework, the same probabilistic
model can be used for the two hands working in parallel
and independently, while a rule-based model is proposed to
identify the resulting bimanual action. Finally, limitations and
perspectives of this approach to more complex, full-bimanual
manipulations are discussed.},
	publisher = {IEEE},
	booktitle = {IROS 2022},
	city = {Kyoto}
}