4,210 research outputs found
Human-Robot Trust Integrated Task Allocation and Symbolic Motion planning for Heterogeneous Multi-robot Systems
This paper presents a human-robot trust integrated task allocation and motion
planning framework for multi-robot systems (MRS) in performing a set of tasks
concurrently. A set of task specifications in parallel are conjuncted with MRS
to synthesize a task allocation automaton. Each transition of the task
allocation automaton is associated with the total trust value of human in
corresponding robots. Here, the human-robot trust model is constructed with a
dynamic Bayesian network (DBN) by considering individual robot performance,
safety coefficient, human cognitive workload and overall evaluation of task
allocation. Hence, a task allocation path with maximum encoded human-robot
trust can be searched based on the current trust value of each robot in the
task allocation automaton. Symbolic motion planning (SMP) is implemented for
each robot after they obtain the sequence of actions. The task allocation path
can be intermittently updated with this DBN based trust model. The overall
strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask
automata
PLA2G7/PAF-AH as a protective factor of BRCA1 mutant breast cancer and ovarian cancer and negatively regulate the Wnt signaling pathway
Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation
Automatic parsing of anatomical objects in X-ray images is critical to many
clinical applications in particular towards image-guided invention and workflow
automation. Existing deep network models require a large amount of labeled
data. However, obtaining accurate pixel-wise labeling in X-ray images relies
heavily on skilled clinicians due to the large overlaps of anatomy and the
complex texture patterns. On the other hand, organs in 3D CT scans preserve
clearer structures as well as sharper boundaries and thus can be easily
delineated. In this paper, we propose a novel model framework for learning
automatic X-ray image parsing from labeled CT scans. Specifically, a Dense
Image-to-Image network (DI2I) for multi-organ segmentation is first trained on
X-ray like Digitally Reconstructed Radiographs (DRRs) rendered from 3D CT
volumes. Then we introduce a Task Driven Generative Adversarial Network
(TD-GAN) architecture to achieve simultaneous style transfer and parsing for
unseen real X-ray images. TD-GAN consists of a modified cycle-GAN substructure
for pixel-to-pixel translation between DRRs and X-ray images and an added
module leveraging the pre-trained DI2I to enforce segmentation consistency. The
TD-GAN framework is general and can be easily adapted to other learning tasks.
In the numerical experiments, we validate the proposed model on 815 DRRs and
153 topograms. While the vanilla DI2I without any adaptation fails completely
on segmenting the topograms, the proposed model does not require any topogram
labels and is able to provide a promising average dice of 85% which achieves
the same level accuracy of supervised training (88%)
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