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In this work we develop and demonstrate a probabilistic generative model for phytoplankton communities. The proposed model takes counts of a set of phytoplankton taxa in a timeseries as its training data, and models communities by learning sparse co-occurrence structure between the taxa. Our model is probabilistic, where communities are represented by probability distributions over the species, and...
Many interesting natural phenomena are sparsely distributed and discrete. Locating the hotspots of such sparsely distributed phenomena is often difficult because their density gradient is likely to be very noisy. We present a novel approach to this search problem, where we model the co-occurrence relations between a robot's observations with a Bayesian nonparametric topic model. This approach makes...
We propose and evaluate a method for learning deep-sea substrate types using video recorded with a remotely operated vehicle (ROV). The goal of this work is to create a labelled spatial map of substrate types from ROV video in order to support biological and geological domain research. The output of our method describes the mixtures of geological features such as sediment and types of lava flow in...
In this paper we address the rendezvous problem between an autonomous underwater vehicle (AUV) and a passively floating drifter on the sea surface. The AUV's mission is to keep an estimate of the floating drifter's position while exploring the underwater environment and periodically attempting to rendezvous with it. We are interested in the case where the AUV loses track of the drifter, predicts its...
The problem of Adaptation from Participation (AfP) aims to improve the efficiency of a human-robot team by adapting a robot's autonomous systems and behaviors based on command-level input from a human supervisor. As a solution to AfP, the Adaptive Parameter EXploration (APEX) algorithm continuously explores the space of all possible parameter configurations for the robot's autonomous system in an...
We discuss the problem of automatically discovering different acoustic regions in the world, and then labeling the trajectory of a robot using these region labels. We use quantized Mel Frequency Cepstral Coefficients (MFCC) as low level features, and a temporally smoothed variant of Latent Dirichlet Allocation (LDA) to compute both the region models, and most likely region labels associated with each...
In this paper we describe a heterogeneous multi-robot system for assisting scientists in environmental monitoring tasks, such as the inspection of marine ecosystems. This team of robots is comprised of a fixed-wing aerial vehicle, an autonomous airboat, and an agile legged underwater robot. These robots interact with off-site scientists and operate in a hierarchical structure to autonomously collect...
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