31,791 research outputs found
Learning to Singulate Objects using a Push Proposal Network
Learning to act in unstructured environments, such as cluttered piles of
objects, poses a substantial challenge for manipulation robots. We present a
novel neural network-based approach that separates unknown objects in clutter
by selecting favourable push actions. Our network is trained from data
collected through autonomous interaction of a PR2 robot with randomly organized
tabletop scenes. The model is designed to propose meaningful push actions based
on over-segmented RGB-D images. We evaluate our approach by singulating up to 8
unknown objects in clutter. We demonstrate that our method enables the robot to
perform the task with a high success rate and a low number of required push
actions. Our results based on real-world experiments show that our network is
able to generalize to novel objects of various sizes and shapes, as well as to
arbitrary object configurations. Videos of our experiments can be viewed at
http://robotpush.cs.uni-freiburg.deComment: International Symposium on Robotics Research (ISRR) 2017, videos:
http://robotpush.cs.uni-freiburg.d
iMapD: intrinsic Map Dynamics exploration for uncharted effective free energy landscapes
We describe and implement iMapD, a computer-assisted approach for
accelerating the exploration of uncharted effective Free Energy Surfaces (FES),
and more generally for the extraction of coarse-grained, macroscopic
information from atomistic or stochastic (here Molecular Dynamics, MD)
simulations. The approach functionally links the MD simulator with nonlinear
manifold learning techniques. The added value comes from biasing the simulator
towards new, unexplored phase space regions by exploiting the smoothness of the
(gradually, as the exploration progresses) revealed intrinsic low-dimensional
geometry of the FES
THE COLUMBUS GROUND SEGMENT – A PRECURSOR FOR FUTURE MANNED MISSIONS
In the beginning the space programs were self standing national activities, often in competition to other nations. Today space flight becomes more and more an international task. Complex space mission and deep space explorations are not longer to be stemmed by one agency or nation alone but are joint activities of several nations. The best example for such a joint (ad-) venture at the moment is the International Space Station ISS.
Such international activities define complete new requirements for the supporting ground segments. The world-wide distribution of a ground segment is not any longer limited to a network of ground stations with the aim to provide a good coverage of the space craft. The coverage is sometimes – like for the ISSanyway ensured by using a relay satellite system instead. In addition to the enhanced down- and uplink methods a ground segment is aimed to connect the different centres of competence of all participating agencies/nations.
From the space craft operations point of view such transnational ground segments are required to support distributed and shared operations in a predefined decision/commanding hierarchy. This has to be taken into account in the technical topology as well as for the operational set-up and teaming.
Last not least increases the duration of missions, which requires a certain flexibility of the ground segment and long-term maintenance strategies for the ground segment with a special emphasis on nonintrusive replacements. The Russian space station MIR has been in the orbit for about 15 years, the ISS is currently targeted for 2020, to be for over 20 years in space
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
Selecting optimal parameters for a neural network architecture can often make
the difference between mediocre and state-of-the-art performance. However,
little is published which parameters and design choices should be evaluated or
selected making the correct hyperparameter optimization often a "black art that
requires expert experiences" (Snoek et al., 2012). In this paper, we evaluate
the importance of different network design choices and hyperparameters for five
common linguistic sequence tagging tasks (POS, Chunking, NER, Entity
Recognition, and Event Detection). We evaluated over 50.000 different setups
and found, that some parameters, like the pre-trained word embeddings or the
last layer of the network, have a large impact on the performance, while other
parameters, for example the number of LSTM layers or the number of recurrent
units, are of minor importance. We give a recommendation on a configuration
that performs well among different tasks.Comment: 34 pages. 9 page version of this paper published at EMNLP 201
A Millisecond Interferometric Search for Fast Radio Bursts with the Very Large Array
We report on the first millisecond timescale radio interferometric search for
the new class of transient known as fast radio bursts (FRBs). We used the Very
Large Array (VLA) for a 166-hour, millisecond imaging campaign to detect and
precisely localize an FRB. We observed at 1.4 GHz and produced visibilities
with 5 ms time resolution over 256 MHz of bandwidth. Dedispersed images were
searched for transients with dispersion measures from 0 to 3000 pc/cm3. No
transients were detected in observations of high Galactic latitude fields taken
from September 2013 though October 2014. Observations of a known pulsar show
that images typically had a thermal-noise limited sensitivity of 120 mJy/beam
(8 sigma; Stokes I) in 5 ms and could detect and localize transients over a
wide field of view. Our nondetection limits the FRB rate to less than
7e4/sky/day (95% confidence) above a fluence limit of 1.2 Jy-ms. Assuming a
Euclidean flux distribution, the VLA rate limit is inconsistent with the
published rate of Thornton et al. We recalculate previously published rates
with a homogeneous consideration of the effects of primary beam attenuation,
dispersion, pulse width, and sky brightness. This revises the FRB rate downward
and shows that the VLA observations had a roughly 60% chance of detecting a
typical FRB and that a 95% confidence constraint would require roughly 500
hours of similar VLA observing. Our survey also limits the repetition rate of
an FRB to 2 times less than any known repeating millisecond radio transient.Comment: Submitted to ApJ. 13 pages, 9 figure
On-the-fly Approximation of Multivariate Total Variation Minimization
In the context of change-point detection, addressed by Total Variation
minimization strategies, an efficient on-the-fly algorithm has been designed
leading to exact solutions for univariate data. In this contribution, an
extension of such an on-the-fly strategy to multivariate data is investigated.
The proposed algorithm relies on the local validation of the Karush-Kuhn-Tucker
conditions on the dual problem. Showing that the non-local nature of the
multivariate setting precludes to obtain an exact on-the-fly solution, we
devise an on-the-fly algorithm delivering an approximate solution, whose
quality is controlled by a practitioner-tunable parameter, acting as a
trade-off between quality and computational cost. Performance assessment shows
that high quality solutions are obtained on-the-fly while benefiting of
computational costs several orders of magnitude lower than standard iterative
procedures. The proposed algorithm thus provides practitioners with an
efficient multivariate change-point detection on-the-fly procedure
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