71 research outputs found
Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search
In principle, reinforcement learning and policy search methods can enable
robots to learn highly complex and general skills that may allow them to
function amid the complexity and diversity of the real world. However, training
a policy that generalizes well across a wide range of real-world conditions
requires far greater quantity and diversity of experience than is practical to
collect with a single robot. Fortunately, it is possible for multiple robots to
share their experience with one another, and thereby, learn a policy
collectively. In this work, we explore distributed and asynchronous policy
learning as a means to achieve generalization and improved training times on
challenging, real-world manipulation tasks. We propose a distributed and
asynchronous version of Guided Policy Search and use it to demonstrate
collective policy learning on a vision-based door opening task using four
robots. We show that it achieves better generalization, utilization, and
training times than the single robot alternative.Comment: Submitted to the IEEE International Conference on Robotics and
Automation 201
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
Learning-based approaches to robotic manipulation are limited by the
scalability of data collection and accessibility of labels. In this paper, we
present a multi-task domain adaptation framework for instance grasping in
cluttered scenes by utilizing simulated robot experiments. Our neural network
takes monocular RGB images and the instance segmentation mask of a specified
target object as inputs, and predicts the probability of successfully grasping
the specified object for each candidate motor command. The proposed transfer
learning framework trains a model for instance grasping in simulation and uses
a domain-adversarial loss to transfer the trained model to real robots using
indiscriminate grasping data, which is available both in simulation and the
real world. We evaluate our model in real-world robot experiments, comparing it
with alternative model architectures as well as an indiscriminate grasping
baseline.Comment: ICRA 201
ON DEMAND DIGITAL CARD DISPLAY
Disclosed herein is an outline of processes for accessing account information linked to a payment card, free from dependence on an issuer application. In this process a computing device first acquires a deep link from the payment network and then receives input from the user. Subsequently, an application clip is invoked in response to the user input, prompting the user for identity verification. Upon successful verification, the account information is displayed through the application clip, deep link or with in the native application
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping
The distributional perspective on reinforcement learning (RL) has given rise
to a series of successful Q-learning algorithms, resulting in state-of-the-art
performance in arcade game environments. However, it has not yet been analyzed
how these findings from a discrete setting translate to complex practical
applications characterized by noisy, high dimensional and continuous
state-action spaces. In this work, we propose Quantile QT-Opt (Q2-Opt), a
distributional variant of the recently introduced distributed Q-learning
algorithm for continuous domains, and examine its behaviour in a series of
simulated and real vision-based robotic grasping tasks. The absence of an actor
in Q2-Opt allows us to directly draw a parallel to the previous discrete
experiments in the literature without the additional complexities induced by an
actor-critic architecture. We demonstrate that Q2-Opt achieves a superior
vision-based object grasping success rate, while also being more sample
efficient. The distributional formulation also allows us to experiment with
various risk distortion metrics that give us an indication of how robots can
concretely manage risk in practice using a Deep RL control policy. As an
additional contribution, we perform batch RL experiments in our virtual
environment and compare them with the latest findings from discrete settings.
Surprisingly, we find that the previous batch RL findings from the literature
obtained on arcade game environments do not generalise to our setup.Comment: Camera-ready version for RSS 2020. Contains 8 pages, 7 figure
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Instrumenting and collecting annotated visual grasping datasets to train
modern machine learning algorithms can be extremely time-consuming and
expensive. An appealing alternative is to use off-the-shelf simulators to
render synthetic data for which ground-truth annotations are generated
automatically. Unfortunately, models trained purely on simulated data often
fail to generalize to the real world. We study how randomized simulated
environments and domain adaptation methods can be extended to train a grasping
system to grasp novel objects from raw monocular RGB images. We extensively
evaluate our approaches with a total of more than 25,000 physical test grasps,
studying a range of simulation conditions and domain adaptation methods,
including a novel extension of pixel-level domain adaptation that we term the
GraspGAN. We show that, by using synthetic data and domain adaptation, we are
able to reduce the number of real-world samples needed to achieve a given level
of performance by up to 50 times, using only randomly generated simulated
objects. We also show that by using only unlabeled real-world data and our
GraspGAN methodology, we obtain real-world grasping performance without any
real-world labels that is similar to that achieved with 939,777 labeled
real-world samples.Comment: 9 pages, 5 figures, 3 table
Integrative disease classification based on cross-platform microarray data
<p>Abstract</p> <p>Background</p> <p>Disease classification has been an important application of microarray technology. However, most microarray-based classifiers can only handle data generated within the same study, since microarray data generated by different laboratories or with different platforms can not be compared directly due to systematic variations. This issue has severely limited the practical use of microarray-based disease classification.</p> <p>Results</p> <p>In this study, we tested the feasibility of disease classification by integrating the large amount of heterogeneous microarray datasets from the public microarray repositories. Cross-platform data compatibility is created by deriving expression log-rank ratios within datasets. One may then compare vectors of log-rank ratios across datasets. In addition, we systematically map textual annotations of datasets to concepts in Unified Medical Language System (UMLS), permitting quantitative analysis of the phenotype "distance" between datasets and automated construction of disease classes. We design a new classification approach named ManiSVM, which integrates Manifold data transformation with SVM learning to exploit the data properties. Using the leave one dataset out cross validation, ManiSVM achieved the overall accuracy of 70.7% (68.6% precision and 76.9% recall) with many disease classes achieving the accuracy higher than 80%.</p> <p>Conclusion</p> <p>Our results not only demonstrated the feasibility of the integrated disease classification approach, but also showed that the classification accuracy increases with the number of homogenous training datasets. Thus, the power of the integrative approach will increase with the continuous accumulation of microarray data in public repositories. Our study shows that automated disease diagnosis can be an important and promising application of the enormous amount of costly to generate, yet freely available, public microarray data.</p
OBO (ON BEHALF OF) MULTI FACTOR AUTHENTICATION
The present disclosure relates to an authentication method to enable a person to authenticate transaction on behalf of the cardholder. On behalf of (OBO) authentication allows the card holder to designate an authorized user to authenticate on behalf of them for multi factor authentication, login or any other operation. When the cardholder initiates the transaction, a request to authorize the transaction may be sent to the authorized user, and the authorize user may approve the transaction on behalf of the cardholder
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