34 research outputs found
Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models
The gap between our ability to collect interesting data and our ability to
analyze these data is growing at an unprecedented rate. Recent algorithmic
attempts to fill this gap have employed unsupervised tools to discover
structure in data. Some of the most successful approaches have used
probabilistic models to uncover latent thematic structure in discrete data.
Despite the success of these models on textual data, they have not generalized
as well to image data, in part because of the spatial and temporal structure
that may exist in an image stream.
We introduce a novel unsupervised machine learning framework that
incorporates the ability of convolutional autoencoders to discover features
from images that directly encode spatial information, within a Bayesian
nonparametric topic model that discovers meaningful latent patterns within
discrete data. By using this hybrid framework, we overcome the fundamental
dependency of traditional topic models on rigidly hand-coded data
representations, while simultaneously encoding spatial dependency in our topics
without adding model complexity. We apply this model to the motivating
application of high-level scene understanding and mission summarization for
exploratory marine robots. Our experiments on a seafloor dataset collected by a
marine robot show that the proposed hybrid framework outperforms current
state-of-the-art approaches on the task of unsupervised seafloor terrain
characterization.Comment: 8 page
Near-optimal irrevocable sample selection for periodic data streams with applications to marine robotics
We consider the task of monitoring spatiotemporal phenomena in real-time by
deploying limited sampling resources at locations of interest irrevocably and
without knowledge of future observations. This task can be modeled as an
instance of the classical secretary problem. Although this problem has been
studied extensively in theoretical domains, existing algorithms require that
data arrive in random order to provide performance guarantees. These algorithms
will perform arbitrarily poorly on data streams such as those encountered in
robotics and environmental monitoring domains, which tend to have
spatiotemporal structure. We focus on the problem of selecting representative
samples from phenomena with periodic structure and introduce a novel sample
selection algorithm that recovers a near-optimal sample set according to any
monotone submodular utility function. We evaluate our algorithm on a seven-year
environmental dataset collected at the Martha's Vineyard Coastal Observatory
and show that it selects phytoplankton sample locations that are nearly optimal
in an information-theoretic sense for predicting phytoplankton concentrations
in locations that were not directly sampled. The proposed periodic secretary
algorithm can be used with theoretical performance guarantees in many real-time
sensing and robotics applications for streaming, irrevocable sample selection
from periodic data streams.Comment: 8 pages, accepted for presentation in IEEE Int. Conf. on Robotics and
Automation, ICRA '18, Brisbane, Australia, May 201
Phytoplankton Hotspot Prediction With an Unsupervised Spatial Community Model
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 it possible to produce a robust estimate of the spatial
distribution of the target, even in the absence of direct target observations.
We apply the proposed approach to the problem of finding the spatial locations
of the hotspots of a specific phytoplankton taxon in the ocean. We use
classified image data from Imaging FlowCytobot (IFCB), which automatically
measures individual microscopic cells and colonies of cells. Given these
individual taxon-specific observations, we learn a phytoplankton community
model that characterizes the co-occurrence relations between taxa. We present
experiments with simulated robot missions drawn from real observation data
collected during a research cruise traversing the US Atlantic coast. Our
results show that the proposed approach outperforms nearest neighbor and
k-means based methods for predicting the spatial distribution of hotspots from
in-situ observations.Comment: To appear in ICRA 2017, Singapor
Active Reward Learning for Co-Robotic Vision Based Exploration in Bandwidth Limited Environments
We present a novel POMDP problem formulation for a robot that must
autonomously decide where to go to collect new and scientifically relevant
images given a limited ability to communicate with its human operator. From
this formulation we derive constraints and design principles for the
observation model, reward model, and communication strategy of such a robot,
exploring techniques to deal with the very high-dimensional observation space
and scarcity of relevant training data. We introduce a novel active reward
learning strategy based on making queries to help the robot minimize path
"regret" online, and evaluate it for suitability in autonomous visual
exploration through simulations. We demonstrate that, in some bandwidth-limited
environments, this novel regret-based criterion enables the robotic explorer to
collect up to 17% more reward per mission than the next-best criterion.Comment: 7 pages, 4 figures; accepted for presentation in IEEE Int. Conf. on
Robotics and Automation, ICRA '20, Paris, France, June 202
Semi-Supervised Visual Tracking of Marine Animals using Autonomous Underwater Vehicles
In-situ visual observations of marine organisms is crucial to developing
behavioural understandings and their relations to their surrounding ecosystem.
Typically, these observations are collected via divers, tags, and
remotely-operated or human-piloted vehicles. Recently, however, autonomous
underwater vehicles equipped with cameras and embedded computers with GPU
capabilities are being developed for a variety of applications, and in
particular, can be used to supplement these existing data collection mechanisms
where human operation or tags are more difficult. Existing approaches have
focused on using fully-supervised tracking methods, but labelled data for many
underwater species are severely lacking. Semi-supervised trackers may offer
alternative tracking solutions because they require less data than
fully-supervised counterparts. However, because there are not existing
realistic underwater tracking datasets, the performance of semi-supervised
tracking algorithms in the marine domain is not well understood. To better
evaluate their performance and utility, in this paper we provide (1) a novel
dataset specific to marine animals located at http://warp.whoi.edu/vmat/, (2)
an evaluation of state-of-the-art semi-supervised algorithms in the context of
underwater animal tracking, and (3) an evaluation of real-world performance
through demonstrations using a semi-supervised algorithm on-board an autonomous
underwater vehicle to track marine animals in the wild.Comment: To appear in IJCV SI: Animal Trackin