@inproceedings {pub4261pub4412,
	title = {Accuracy Estimation for an Incrementally Learning
Cooperative Inventory Assistant Robot},
	author = {Christian  Limberg AND Heiko Wersing AND Helge Ritter},
	year = {2020},
	month = {November},
	abstract = {Interactive teaching from a human can be applied
to extend the knowledge of a service robot according to
novel task demands. This is particularly attractive if it is
either inefficient or not feasible to pre-train all relevant object
knowledge beforehand. Like in a normal human teacher and
student situation it is then vital to estimate the learning progress
of the robot in order to judge its competence in carrying out the
desired task. While observing robot task success and failure is a
straightforward option, there are more efficient alternatives. In
this contribution we investigate the application of a recent semi-
supervised confidence-based approach to accuracy estimation
towards incremental object learning for an inventory assistant
robot. We evaluate the approach and demonstrate its applica-
bility in a slightly simplified, but realistic setting. We show that
the configram estimation model (CGEM) outperforms standard
approaches for accuracy estimation like cross-validation and
interleaved test/train error for active learning scenarios, thus
minimizing human training effort.},
	publisher = {Springer, LNCS, LNAI, LNBI},
	booktitle = {International Conference on Neural Information Processing ICONIP 2020}
}