Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation
Conference Paper, Proceedings of (CoRL) Conference on Robot Learning, Vol. 78, pp. 249 - 261, November, 2017
Abstract
We proposed a multimodal end-to-end policy based on deep reinforcement learning (DRL) that leverages sensor fusion to reduced performance drops in noisy environment from 50% to 10% compared with the baseline and makes the policy functional even in the face of partial sensor failure by using a novel stochastic technique called Sensor Dropout to reduce sensitivity to any sensor subset, and a new auxiliary loss on policy network along with standard DRL loss that reduces the action variations.
BibTeX
@conference{Liu-2017-119999,author = {G. H. Liu and A. Siravuru and S. Prabhakar and M. Veloso and G. Kantor},
title = {Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation},
booktitle = {Proceedings of (CoRL) Conference on Robot Learning},
year = {2017},
month = {November},
volume = {78},
pages = {249 - 261},
}
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.