Approximate Distributed Spatiotemporal Topic Models for Multi-Robot Terrain Characterization
Name
kdoherty_iros2018.pdf
Description
Accepted version
Size
4.4 MB
Format
Adobe PDF
Checksum (MD5)
15f48a6ceb67e9b98a1b169e8f748dd8
Author(s)
Doherty, Kevin
Flaspohler, Genevieve Elaine
Roy, Nicholas
Girdhar, Yogesh
Date Issued
January 2019
Journal
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Doherty, Kevin et al. "Approximate Distributed Spatiotemporal Topic Models for Multi-Robot Terrain Characterization." IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2018, Institute of Electrical and Electronics Engineers, January 2019 © 2018 IEEE
Version
Author's final manuscript
Abstract
Unsupervised learning techniques, such as Bayesian topic models, are capable of discovering latent structure directly from raw data. These unsupervised models can endow robots with the ability to learn from their observations without human supervision, and then use the learned models for tasks such as autonomous exploration, adaptive sampling, or surveillance. This paper extends single-robot topic models to the domain of multiple robots. The main difficulty of this extension lies in achieving and maintaining global consensus among the unsupervised models learned locally by each robot. This is especially challenging for multi-robot teams operating in communication-constrained environments, such as marine robots. We present a novel approach for multi-robot distributed learning in which each robot maintains a local topic model to categorize its observations and model parameters are shared to achieve global consensus. We apply a combinatorial optimization procedure that combines local robot topic distributions into a globally consistent model based on topic similarity, which we find mitigates topic drift when compared to a baseline approach that matches topics naïvely, We evaluate our methods experimentally by demonstrating multi-robot underwater terrain characterization using simulated missions on real seabed imagery. Our proposed method achieves similar model quality under bandwidth-constraints to that achieved by models that continuously communicate, despite requiring less than one percent of the data transmission needed for continuous communication.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Woods Hole Oceanographic Institution
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros.2018.8594442
