EValueAction
a proposal for policy evaluation in simulation to support interactive imitation learning
Fiorella Sibona (Politecnico di Torino)
Jelle Luijkx (TU Delft - Learning & Autonomous Control)
Bas Van Der Heijden (TU Delft - Learning & Autonomous Control)
Laura Ferranti (TU Delft - Learning & Autonomous Control)
Marina Indri (Politecnico di Torino)
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Abstract
The up-and-coming concept of Industry 5.0 fore-sees 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.