@inproceedings{sibona_evalueaction_2023,
	title = {{EValueAction}: a proposal for policy evaluation in simulation to support interactive imitation learning},
	url = {paper=https://r2clab.com/wp-content/uploads/2023/06/Paper_EVA_2023_acks.pdf},
	abstract = {The up-and-coming concept of Industry 5.0 foresees
human-centric flexible production lines, where collaborative
robots support human workforce. In order to allow a seamless
collaboration between intelligent robots and human workers,
designing solutions for non-expert users is crucial. Learning from
demonstration emerged as the enabling approach to address such
a problem. However, more focus should be put on finding safe
solutions which optimize the cost associated with the demonstrations
collection process. This paper introduces a preliminary outline
of a system, namely EValueAction (EVA), designed to assist
the human in the process of collecting interactive demonstrations
taking advantage of simulation to safely avoid failures. A policy
is pre-trained with human-demonstrations and, where needed,
new informative data are interactively gathered and aggregated
to iteratively improve the initial policy. A trial case study further
reinforces the relevance of the work by demonstrating the crucial
role of informative demonstrations for generalization.},
	booktitle = {{IEEE} {INDIN} 2023},
	author = {Sibona, F. and Luijkx, J. and van der Heijden, B. and Ferranti, L. and Indri, M.},
	year = {2023},
}

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