75 research outputs found
Online Robot Introspection via Wrench-based Action Grammars
Robotic failure is all too common in unstructured robot tasks. Despite
well-designed controllers, robots often fail due to unexpected events. How do
robots measure unexpected events? Many do not. Most robots are driven by the
sense-plan act paradigm, however more recently robots are undergoing a
sense-plan-act-verify paradigm. In this work, we present a principled
methodology to bootstrap online robot introspection for contact tasks. In
effect, we are trying to enable the robot to answer the question: what did I
do? Is my behavior as expected or not? To this end, we analyze noisy wrench
data and postulate that the latter inherently contains patterns that can be
effectively represented by a vocabulary. The vocabulary is generated by
segmenting and encoding the data. When the wrench information represents a
sequence of sub-tasks, we can think of the vocabulary forming a sentence (set
of words with grammar rules) for a given sub-task; allowing the latter to be
uniquely represented. The grammar, which can also include unexpected events,
was classified in offline and online scenarios as well as for simulated and
real robot experiments. Multiclass Support Vector Machines (SVMs) were used
offline, while online probabilistic SVMs were are used to give temporal
confidence to the introspection result. The contribution of our work is the
presentation of a generalizable online semantic scheme that enables a robot to
understand its high-level state whether nominal or abnormal. It is shown to
work in offline and online scenarios for a particularly challenging contact
task: snap assemblies. We perform the snap assembly in one-arm simulated and
real one-arm experiments and a simulated two-arm experiment. This verification
mechanism can be used by high-level planners or reasoning systems to enable
intelligent failure recovery or determine the next most optima manipulation
skill to be used.Comment: arXiv admin note: substantial text overlap with arXiv:1609.0494
Illumination protocols for non-linear phononics in bismuth and antimony
We study the optical generation and control of coherent phonons in elemental
bismuth (Bi) and antimony (Sb) using a classical equation of motion informed by
first-principles calculations of the potential energy surface and the
frequency-dependent macroscopic dielectric function along the zone-centered
optical phonons coordinates. Using this approach, we demonstrate that phonons
with the largest optomechanical couplings, also have the strongest degree of
anharmonicity, a result of the broken symmetry structural ground state of Bi
and Sb. We show how this anharmonicity, explaining the light-induced phonon
softening observed in experiments, prevents the application of standard
phonon-amplification and annihilation protocols. We introduce a simple
linearization protocol that extends the use of such protocols to the case of
anharmonic phonons in broken symmetry materials, and demonstrate its efficiency
at high displacement amplitudes. Our formalism and results provide a path for
improving optical control in non-linear phononics
Text detection and recognition based on a lensless imaging system
Lensless cameras are characterized by several advantages (e.g.,
miniaturization, ease of manufacture, and low cost) as compared with
conventional cameras. However, they have not been extensively employed due to
their poor image clarity and low image resolution, especially for tasks that
have high requirements on image quality and details such as text detection and
text recognition. To address the problem, a framework of deep-learning-based
pipeline structure was built to recognize text with three steps from raw data
captured by employing lensless cameras. This pipeline structure consisted of
the lensless imaging model U-Net, the text detection model connectionist text
proposal network (CTPN), and the text recognition model convolutional recurrent
neural network (CRNN). Compared with the method focusing only on image
reconstruction, UNet in the pipeline was able to supplement the imaging details
by enhancing factors related to character categories in the reconstruction
process, so the textual information can be more effectively detected and
recognized by CTPN and CRNN with fewer artifacts and high-clarity reconstructed
lensless images. By performing experiments on datasets of different
complexities, the applicability to text detection and recognition on lensless
cameras was verified. This study reasonably demonstrates text detection and
recognition tasks in the lensless camera system,and develops a basic method for
novel applications
Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods
With the popularity of cryptocurrencies and the remarkable development of
blockchain technology, decentralized applications emerged as a revolutionary
force for the Internet. Meanwhile, decentralized applications have also
attracted intense attention from the online gambling community, with more and
more decentralized gambling platforms created through the help of smart
contracts. Compared with conventional gambling platforms, decentralized
gambling have transparent rules and a low participation threshold, attracting a
substantial number of gamblers. In order to discover gambling behaviors and
identify the contracts and addresses involved in gambling, we propose a tool
termed ETHGamDet. The tool is able to automatically detect the smart contracts
and addresses involved in gambling by scrutinizing the smart contract code and
address transaction records. Interestingly, we present a novel LightGBM model
with memory components, which possesses the ability to learn from its own
misclassifications. As a side contribution, we construct and release a
large-scale gambling dataset at
https://github.com/AwesomeHuang/Bitcoin-Gambling-Dataset to facilitate future
research in this field. Empirically, ETHGamDet achieves a F1-score of 0.72 and
0.89 in address classification and contract classification respectively, and
offers novel and interesting insights
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