148 research outputs found
Learning Ground Traversability from Simulations
Mobile ground robots operating on unstructured terrain must predict which
areas of the environment they are able to pass in order to plan feasible paths.
We address traversability estimation as a heightmap classification problem: we
build a convolutional neural network that, given an image representing the
heightmap of a terrain patch, predicts whether the robot will be able to
traverse such patch from left to right. The classifier is trained for a
specific robot model (wheeled, tracked, legged, snake-like) using simulation
data on procedurally generated training terrains; the trained classifier can be
applied to unseen large heightmaps to yield oriented traversability maps, and
then plan traversable paths. We extensively evaluate the approach in simulation
on six real-world elevation datasets, and run a real-robot validation in one
indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation
Electronic phase separation near the superconductor-insulator transition of Nd1+xBa2−xCu3O7−δ thin films studied by an electric-field-induced doping effect
We report a detailed study of the transport properties of Nd(1+x)Ba(2-x)Cu(3)O(7-delta) thin films with doping changed by field effect. The data cover the whole superconducting to insulating transition and show remarkable Similarities with the effect of chemical doping in high critical temperature superconductors. The results suggest that the add-on of carriers is accompanied by an electronic phase separation, independent on the details of the doping mechanism
Challenges in Visual Anomaly Detection for Mobile Robots
We consider the task of detecting anomalies for autonomous mobile robots
based on vision. We categorize relevant types of visual anomalies and discuss
how they can be detected by unsupervised deep learning methods. We propose a
novel dataset built specifically for this task, on which we test a
state-of-the-art approach; we finally discuss deployment in a real scenario.Comment: Workshop paper presented at the ICRA 2022 Workshop on Safe and
Reliable Robot Autonomy under Uncertainty
https://sites.google.com/umich.edu/saferobotautonomy/hom
Electric field effect and superconducting–insulating transition in ‘123’ cuprate superconductors
The physics of high critical temperature superconductors (HTS) remains a fascinating but undisclosed issue in condensed matter. One of the most interesting topics is the transition from the insulating phase of the parent compound, having long range antiferromagnetic order, to the superconducting phase. A method to investigate in detail the superconducting to insulating (SIT) transition in HTS is to control the doping of the CuO(2) planes in a fine way. Here, by using the electric field effect on thin Nd(1)Ba(2)Cu(3)O(7) films, we present a study of the HTS phase diagram close to the SIT with unprecedented detail. By virtue of these data, we will show that doping of holes in samples located at the boundary separating the superconducting and insulating regions produces changes in the transport characteristic consistent with an electronic phase separation scenario. Some consequences of these data are the failure of standard 2D quantum scaling theory and the possible coexistence of superconducting and weakly insulating phases in this region of the phase diagram. A continuous transition between the two competing phases as a function of doping place evident constraints on the mechanism of superconductivity
Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition
Good old on-line back-propagation for plain multi-layer perceptrons yields a
very low 0.35% error rate on the famous MNIST handwritten digits benchmark. All
we need to achieve this best result so far are many hidden layers, many neurons
per layer, numerous deformed training images, and graphics cards to greatly
speed up learning.Comment: 14 pages, 2 figures, 4 listing
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