Approximate Distributed Spatiotemporal Topic Models for Multi-Robot Terrain Characterization

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.

Authors

• Kevin J. Doherty 0001
• Genevieve Flaspohler
• Nicholas Roy
• Yogesh A. Girdhar

Keywords

• Adaptation models
• Robot sensing systems
• Spatiotemporal phenomena
• Data models
• Visualization
• Mathematical model
• Topic Modeling
• Spatiotemporal Model
• Terrain Characterization
• Unsupervised Learning
• Topic Distribution
• Unsupervised Model
• Continuous Communication
• Adaptive Sampling
• Distributed Learning
• Unsupervised Learning Techniques
• Human Supervision
• Global Consistency
• Non-parametric
• Global Model
• Water Column
• Mutual Information
• Image Frames
• Gibbs Sampling
• Squared Euclidean Distance
• Autonomous Underwater Vehicles
• Latent Dirichlet Allocation
• Swarm Robotics
• Global Update
• Sum Of Squared Differences
• Global Synchronization
• Visual Classification
• Multiple Vehicles
• Communication Constraints
• Variational Inference