58 research outputs found
Sampling-based Motion Planning for Active Multirotor System Identification
This paper reports on an algorithm for planning trajectories that allow a
multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown
parameters. In many problems like self calibration or model parameter
identification some states are only observable under a specific motion. These
motions are often hard to find, especially for inexperienced users. Therefore,
we consider system model identification in an active setting, where the vehicle
autonomously decides what actions to take in order to quickly identify the
model. Our algorithm approximates the belief dynamics of the system around a
candidate trajectory using an extended Kalman filter (EKF). It uses
sampling-based motion planning to explore the space of possible beliefs and
find a maximally informative trajectory within a user-defined budget. We
validate our method in simulation and on a real system showing the feasibility
and repeatability of the proposed approach. Our planner creates trajectories
which reduce model parameter convergence time and uncertainty by a factor of
four.Comment: Published at ICRA 2017. Video available at
https://www.youtube.com/watch?v=xtqrWbgep5
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
Large-Margin Determinantal Point Processes
Determinantal point processes (DPPs) offer a powerful approach to modeling
diversity in many applications where the goal is to select a diverse subset. We
study the problem of learning the parameters (the kernel matrix) of a DPP from
labeled training data. We make two contributions. First, we show how to
reparameterize a DPP's kernel matrix with multiple kernel functions, thus
enhancing modeling flexibility. Second, we propose a novel parameter estimation
technique based on the principle of large margin separation. In contrast to the
state-of-the-art method of maximum likelihood estimation, our large-margin loss
function explicitly models errors in selecting the target subsets, and it can
be customized to trade off different types of errors (precision vs. recall).
Extensive empirical studies validate our contributions, including applications
on challenging document and video summarization, where flexibility in modeling
the kernel matrix and balancing different errors is indispensable.Comment: 15 page
Streaming Non-monotone Submodular Maximization: Personalized Video Summarization on the Fly
The need for real time analysis of rapidly producing data streams (e.g.,
video and image streams) motivated the design of streaming algorithms that can
efficiently extract and summarize useful information from massive data "on the
fly". Such problems can often be reduced to maximizing a submodular set
function subject to various constraints. While efficient streaming methods have
been recently developed for monotone submodular maximization, in a wide range
of applications, such as video summarization, the underlying utility function
is non-monotone, and there are often various constraints imposed on the
optimization problem to consider privacy or personalization. We develop the
first efficient single pass streaming algorithm, Streaming Local Search, that
for any streaming monotone submodular maximization algorithm with approximation
guarantee under a collection of independence systems ,
provides a constant approximation guarantee for maximizing a
non-monotone submodular function under the intersection of and
knapsack constraints. Our experiments show that for video summarization, our
method runs more than 1700 times faster than previous work, while maintaining
practically the same performance
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