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2021 | OriginalPaper | Chapter

Critic Guided Segmentation of Rewarding Objects in First-Person Views

Authors : Andrew Melnik, Augustin Harter, Christian Limberg, Krishan Rana, Niko Sünderhauf, Helge Ritter

Published in: KI 2021: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

This work discusses a learning approach to mask rewarding objects in images using sparse reward signals from an imitation learning dataset. For that we train an Hourglass network using only feedback from a critic model. The Hourglass network learns to produce a mask to decrease the critic’s score of a high score image and increase the critic’s score of a low score image by swapping the masked areas between these two images. We trained the model on an imitation learning dataset from the NeurIPS 2020 MineRL Competition Track, where our model learned to mask rewarding objects in a complex interactive 3D environment with a sparse reward signal. This approach was part of the 1st place winning solution in this competition. Video demonstration and code: https://rebrand.ly/critic-guided-segmentation.

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Title: Critic Guided Segmentation of Rewarding Objects in First-Person Views
Authors: Andrew Melnik
Augustin Harter
Christian Limberg
Krishan Rana
Niko Sünderhauf
Helge Ritter
Publisher: Springer International Publishing
Book: KI 2021: Advances in Artificial Intelligence
Print ISBN: 978-3-030-87625-8

Electronic ISBN: 978-3-030-87626-5

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-87626-5_25

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