21,761 research outputs found
Gait learning for soft microrobots controlled by light fields
Soft microrobots based on photoresponsive materials and controlled by light
fields can generate a variety of different gaits. This inherent flexibility can
be exploited to maximize their locomotion performance in a given environment
and used to adapt them to changing conditions. Albeit, because of the lack of
accurate locomotion models, and given the intrinsic variability among
microrobots, analytical control design is not possible. Common data-driven
approaches, on the other hand, require running prohibitive numbers of
experiments and lead to very sample-specific results. Here we propose a
probabilistic learning approach for light-controlled soft microrobots based on
Bayesian Optimization (BO) and Gaussian Processes (GPs). The proposed approach
results in a learning scheme that is data-efficient, enabling gait optimization
with a limited experimental budget, and robust against differences among
microrobot samples. These features are obtained by designing the learning
scheme through the comparison of different GP priors and BO settings on a
semi-synthetic data set. The developed learning scheme is validated in
microrobot experiments, resulting in a 115% improvement in a microrobot's
locomotion performance with an experimental budget of only 20 tests. These
encouraging results lead the way toward self-adaptive microrobotic systems
based on light-controlled soft microrobots and probabilistic learning control.Comment: 8 pages, 7 figures, to appear in the proceedings of the IEEE/RSJ
International Conference on Intelligent Robots and Systems 201
Analytic Performance Modeling and Analysis of Detailed Neuron Simulations
Big science initiatives are trying to reconstruct and model the brain by
attempting to simulate brain tissue at larger scales and with increasingly more
biological detail than previously thought possible. The exponential growth of
parallel computer performance has been supporting these developments, and at
the same time maintainers of neuroscientific simulation code have strived to
optimally and efficiently exploit new hardware features. Current state of the
art software for the simulation of biological networks has so far been
developed using performance engineering practices, but a thorough analysis and
modeling of the computational and performance characteristics, especially in
the case of morphologically detailed neuron simulations, is lacking. Other
computational sciences have successfully used analytic performance engineering
and modeling methods to gain insight on the computational properties of
simulation kernels, aid developers in performance optimizations and eventually
drive co-design efforts, but to our knowledge a model-based performance
analysis of neuron simulations has not yet been conducted.
We present a detailed study of the shared-memory performance of
morphologically detailed neuron simulations based on the Execution-Cache-Memory
(ECM) performance model. We demonstrate that this model can deliver accurate
predictions of the runtime of almost all the kernels that constitute the neuron
models under investigation. The gained insight is used to identify the main
governing mechanisms underlying performance bottlenecks in the simulation. The
implications of this analysis on the optimization of neural simulation software
and eventually co-design of future hardware architectures are discussed. In
this sense, our work represents a valuable conceptual and quantitative
contribution to understanding the performance properties of biological networks
simulations.Comment: 18 pages, 6 figures, 15 table
Safe Local Exploration for Replanning in Cluttered Unknown Environments for Micro-Aerial Vehicles
In order to enable Micro-Aerial Vehicles (MAVs) to assist in complex,
unknown, unstructured environments, they must be able to navigate with
guaranteed safety, even when faced with a cluttered environment they have no
prior knowledge of. While trajectory optimization-based local planners have
been shown to perform well in these cases, prior work either does not address
how to deal with local minima in the optimization problem, or solves it by
using an optimistic global planner.
We present a conservative trajectory optimization-based local planner,
coupled with a local exploration strategy that selects intermediate goals. We
perform extensive simulations to show that this system performs better than the
standard approach of using an optimistic global planner, and also outperforms
doing a single exploration step when the local planner is stuck. The method is
validated through experiments in a variety of highly cluttered environments
including a dense forest. These experiments show the complete system running in
real time fully onboard an MAV, mapping and replanning at 4 Hz.Comment: Accepted to ICRA 2018 and RA-L 201
Collider Detection of Dark Matter Electromagnetic Anapole Moments
Dark matter that interacts with the Standard Model by exchanging photons
through higher multipole interactions occurs in a wide range of both strongly
as well as weakly coupled hidden sector models. We study the collider detection
prospects of these candidates, with a focus on Majorana dark matter that
couples through the anapole moment. The study is conducted at the effective
field theory level with the mono- signature incorporating varying levels of
systematic uncertainties at the high-luminosity LHC. The projected collider
reach on the anapole moment is then compared to the reach coming from direct
detection experiments like LZ. Finally, the analysis is applied to a weakly
coupled completion with leptophilic dark matter.Comment: 24 pages, 9 figure
A Comparative Analysis of STM Approaches to Reduction Operations in Irregular Applications
As a recently consolidated paradigm for optimistic concurrency in modern multicore architectures, Transactional Memory (TM)
can help to the exploitation of parallelism in irregular applications when data dependence information is not available up to run-
time. This paper presents and discusses how to leverage TM to exploit parallelism in an important class of irregular applications, the class that exhibits irregular reduction patterns. In order to test and compare our techniques with other solutions, they were implemented in a software TM system called ReduxSTM, that acts as a proof of concept. Basically, ReduxSTM combines two major ideas: a sequential-equivalent ordering of transaction commits that assures the correct result, and an extension of the underlying TM privatization mechanism to reduce unnecessary overhead due to reduction memory updates as well as unnecesary aborts and rollbacks. A comparative study of STM solutions, including ReduxSTM, and other more classical approaches to the parallelization of reduction operations is presented in terms of time, memory and overhead.Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tech
Automatic Loop Kernel Analysis and Performance Modeling With Kerncraft
Analytic performance models are essential for understanding the performance
characteristics of loop kernels, which consume a major part of CPU cycles in
computational science. Starting from a validated performance model one can
infer the relevant hardware bottlenecks and promising optimization
opportunities. Unfortunately, analytic performance modeling is often tedious
even for experienced developers since it requires in-depth knowledge about the
hardware and how it interacts with the software. We present the "Kerncraft"
tool, which eases the construction of analytic performance models for streaming
kernels and stencil loop nests. Starting from the loop source code, the problem
size, and a description of the underlying hardware, Kerncraft can ideally
predict the single-core performance and scaling behavior of loops on multicore
processors using the Roofline or the Execution-Cache-Memory (ECM) model. We
describe the operating principles of Kerncraft with its capabilities and
limitations, and we show how it may be used to quickly gain insights by
accelerated analytic modeling.Comment: 11 pages, 4 figures, 8 listing
Global Continuous Optimization with Error Bound and Fast Convergence
This paper considers global optimization with a black-box unknown objective
function that can be non-convex and non-differentiable. Such a difficult
optimization problem arises in many real-world applications, such as parameter
tuning in machine learning, engineering design problem, and planning with a
complex physics simulator. This paper proposes a new global optimization
algorithm, called Locally Oriented Global Optimization (LOGO), to aim for both
fast convergence in practice and finite-time error bound in theory. The
advantage and usage of the new algorithm are illustrated via theoretical
analysis and an experiment conducted with 11 benchmark test functions. Further,
we modify the LOGO algorithm to specifically solve a planning problem via
policy search with continuous state/action space and long time horizon while
maintaining its finite-time error bound. We apply the proposed planning method
to accident management of a nuclear power plant. The result of the application
study demonstrates the practical utility of our method
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