Learning Interactively to Resolve Ambiguity in Sensor Policy Fusion for Robot

Abstract

This work applies interactive imitation learning for the navigation of a mobile robot. The algorithm"Learning Interactively to Resolve Ambiguity in Sensor Policy Fusion" (LIRA-SPF) is introduced in the field of machine learning for robot navigation. This algorithm extends on existing methods by allowing the ambiguity-free fusion of existing single-sensor policy behavior using an active and interactive querying of the human expert. The ambiguous situations investigated in this work are due the possible perspective mismatch of each sensor: LIRA-SPF aims to detect these situations and save the correct solution in a new fused policy. As a consequence, we provide an alternative to training a new behavior again from scratch, leveraging the knowledge of existing expert behaviors and reducing the required teacher’s effort. The algorithm is tested with different supervised and unsupervised disambiguation strategies thanks to its modular implementation. This paper summarizes multiple simulated and real robot tests, showing the advantages of the proposed disambiguation module on state of the art approaches. In particular, the analysis underlines the necessity of less human-robot interaction during the training process. Finally the conclusions reveal the missing blocks of the approach and how this could be beneficial in the sensor fusion procedure.