12,754 research outputs found
Advancing Robot Autonomy for Long-Horizon Tasks
Autonomous robots have real-world applications in diverse fields, such as
mobile manipulation and environmental exploration, and many such tasks benefit
from a hands-off approach in terms of human user involvement over a long task
horizon. However, the level of autonomy achievable by a deployment is limited
in part by the problem definition or task specification required by the system.
Task specifications often require technical, low-level information that is
unintuitive to describe and may result in generic solutions, burdening the user
technically both before and after task completion. In this thesis, we aim to
advance task specification abstraction toward the goal of increasing robot
autonomy in real-world scenarios. We do so by tackling problems that address
several different angles of this goal. First, we develop a way for the
automatic discovery of optimal transition points between subtasks in the
context of constrained mobile manipulation, removing the need for the human to
hand-specify these in the task specification. We further propose a way to
automatically describe constraints on robot motion by using demonstrated data
as opposed to manually-defined constraints. Then, within the context of
environmental exploration, we propose a flexible task specification framework,
requiring just a set of quantiles of interest from the user that allows the
robot to directly suggest locations in the environment for the user to study.
We next systematically study the effect of including a robot team in the task
specification and show that multirobot teams have the ability to improve
performance under certain specification conditions, including enabling
inter-robot communication. Finally, we propose methods for a communication
protocol that autonomously selects useful but limited information to share with
the other robots.Comment: PhD dissertation. 160 page
The BSM-AI project: SUSY-AI - Generalizing LHC limits on Supersymmetry with Machine Learning
A key research question at the Large Hadron Collider (LHC) is the test of
models of new physics. Testing if a particular parameter set of such a model is
excluded by LHC data is a challenge: It requires the time consuming generation
of scattering events, the simulation of the detector response, the event
reconstruction, cross section calculations and analysis code to test against
several hundred signal regions defined by the ATLAS and CMS experiment. In the
BSM-AI project we attack this challenge with a new approach. Machine learning
tools are thought to predict within a fraction of a millisecond if a model is
excluded or not directly from the model parameters. A first example is SUSY-AI,
trained on the phenomenological supersymmetric standard model (pMSSM). About
300,000 pMSSM model sets - each tested with 200 signal regions by ATLAS - have
been used to train and validate SUSY-AI. The code is currently able to
reproduce the ATLAS exclusion regions in 19 dimensions with an accuracy of at
least 93 percent. It has been validated further within the constrained MSSM and
a minimal natural supersymmetric model, again showing high accuracy. SUSY-AI
and its future BSM derivatives will help to solve the problem of recasting LHC
results for any model of new physics.
SUSY-AI can be downloaded at http://susyai.hepforge.org/. An on-line
interface to the program for quick testing purposes can be found at
http://www.susy-ai.org/
A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation
Cellular electron cryo-tomography enables the 3D visualization of cellular
organization in the near-native state and at submolecular resolution. However,
the contents of cellular tomograms are often complex, making it difficult to
automatically isolate different in situ cellular components. In this paper, we
propose a convolutional autoencoder-based unsupervised approach to provide a
coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate
that the autoencoder can be used for efficient and coarse characterization of
features of macromolecular complexes and surfaces, such as membranes. In
addition, the autoencoder can be used to detect non-cellular features related
to sample preparation and data collection, such as carbon edges from the grid
and tomogram boundaries. The autoencoder is also able to detect patterns that
may indicate spatial interactions between cellular components. Furthermore, we
demonstrate that our autoencoder can be used for weakly supervised semantic
segmentation of cellular components, requiring a very small amount of manual
annotation.Comment: Accepted by Journal of Structural Biolog
Cluster-based feedback control of turbulent post-stall separated flows
We propose a novel model-free self-learning cluster-based control strategy
for general nonlinear feedback flow control technique, benchmarked for
high-fidelity simulations of post-stall separated flows over an airfoil. The
present approach partitions the flow trajectories (force measurements) into
clusters, which correspond to characteristic coarse-grained phases in a
low-dimensional feature space. A feedback control law is then sought for each
cluster state through iterative evaluation and downhill simplex search to
minimize power consumption in flight. Unsupervised clustering of the flow
trajectories for in-situ learning and optimization of coarse-grained control
laws are implemented in an automated manner as key enablers. Re-routing the
flow trajectories, the optimized control laws shift the cluster populations to
the aerodynamically favorable states. Utilizing limited number of sensor
measurements for both clustering and optimization, these feedback laws were
determined in only iterations. The objective of the present work is not
necessarily to suppress flow separation but to minimize the desired cost
function to achieve enhanced aerodynamic performance. The present control
approach is applied to the control of two and three-dimensional separated flows
over a NACA 0012 airfoil with large-eddy simulations at an angle of attack of
, Reynolds number and free-stream Mach number . The optimized control laws effectively minimize the flight power
consumption enabling the flows to reach a low-drag state. The present work aims
to address the challenges associated with adaptive feedback control design for
turbulent separated flows at moderate Reynolds number.Comment: 32 pages, 18 figure
Geometry Processing of Conventionally Produced Mouse Brain Slice Images
Brain mapping research in most neuroanatomical laboratories relies on
conventional processing techniques, which often introduce histological
artifacts such as tissue tears and tissue loss. In this paper we present
techniques and algorithms for automatic registration and 3D reconstruction of
conventionally produced mouse brain slices in a standardized atlas space. This
is achieved first by constructing a virtual 3D mouse brain model from annotated
slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed
model generates ARA-based slice images corresponding to the microscopic images
of histological brain sections. These image pairs are aligned using a geometric
approach through contour images. Histological artifacts in the microscopic
images are detected and removed using Constrained Delaunay Triangulation before
performing global alignment. Finally, non-linear registration is performed by
solving Laplace's equation with Dirichlet boundary conditions. Our methods
provide significant improvements over previously reported registration
techniques for the tested slices in 3D space, especially on slices with
significant histological artifacts. Further, as an application we count the
number of neurons in various anatomical regions using a dataset of 51
microscopic slices from a single mouse brain. This work represents a
significant contribution to this subfield of neuroscience as it provides tools
to neuroanatomist for analyzing and processing histological data.Comment: 14 pages, 11 figure
Assisted Viewpoint Interaction for 3D Visualization
Many three-dimensional visualizations are characterized by the use of a mobile viewpoint that offers multiple perspectives on a set of visual information. To effectively control the viewpoint, the viewer must simultaneously manage the cognitive tasks of understanding the layout of the environment, and knowing where to look to find relevant information, along with mastering the physical interaction required to position the viewpoint in meaningful locations. Numerous systems attempt to address these problems by catering to two extremes: simplified controls or direct presentation. This research attempts to promote hybrid interfaces that offer a supportive, yet unscripted exploration of a virtual environment.Attentive navigation is a specific technique designed to actively redirect viewers' attention while accommodating their independence. User-evaluation shows that this technique effectively facilitates several visualization tasks including landmark recognition, survey knowledge acquisition, and search sensitivity. Unfortunately, it also proves to be excessively intrusive, leading viewers to occasionally struggle for control of the viewpoint. Additional design iterations suggest that formalized coordination protocols between the viewer and the automation can mute the shortcomings and enhance the effectiveness of the initial attentive navigation design.The implications of this research generalize to inform the broader requirements for Human-Automation interaction through the visual channel. Potential applications span a number of fields, including visual representations of abstract information, 3D modeling, virtual environments, and teleoperation experiences
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